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overflow: hidden; text-overflow: ellipsis; -webkit-line-clamp: 3; -webkit-box-orient: vertical; }</style><div class="col-xs-12 clearfix"><div class="u-floatLeft"><h1 class="PageHeader-title u-m0x u-fs30">XML data clustering</h1><div class="u-tcGrayDark">54 Followers</div><div class="u-tcGrayDark u-mt2x">Recent papers in <b>XML data clustering</b></div></div></div></div></div></div><div class="TabbedNavigation"><div class="container"><div class="row"><div class="col-xs-12 clearfix"><ul class="nav u-m0x u-p0x list-inline u-displayFlex"><li class="active"><a href="https://www.academia.edu/Documents/in/XML_data_clustering">Top Papers</a></li><li><a href="https://www.academia.edu/Documents/in/XML_data_clustering/MostCited">Most Cited Papers</a></li><li><a href="https://www.academia.edu/Documents/in/XML_data_clustering/MostDownloaded">Most Downloaded Papers</a></li><li><a href="https://www.academia.edu/Documents/in/XML_data_clustering/MostRecent">Newest Papers</a></li><li><a class="" href="https://www.academia.edu/People/XML_data_clustering">People</a></li></ul></div><style type="text/css">ul.nav{flex-direction:row}@media(max-width: 567px){ul.nav{flex-direction:column}.TabbedNavigation li{max-width:100%}.TabbedNavigation li.active{background-color:var(--background-grey, #dddde2)}.TabbedNavigation li.active:before,.TabbedNavigation li.active:after{display:none}}</style></div></div></div><div class="container"><div class="row"><div class="col-xs-12"><div class="u-displayFlex"><div class="u-flexGrow1"><div class="works"><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_32890776" data-work_id="32890776" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" rel="nofollow" href="https://www.academia.edu/32890776/CLUSTERING_ALGORITHMS_AND_THEIR_APPLICATIONS_IN_CLOUD_COMPUTING_ENVIRONMENT">CLUSTERING ALGORITHMS AND THEIR APPLICATIONS IN CLOUD COMPUTING ENVIRONMENT</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Cloud computing is Internet-based computing that provides shared computer processing resources and data to computers and other devices on demand. Cloud computing is the hottest purpose built architecture created to support computer users.... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_32890776" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Cloud computing is Internet-based computing that provides shared computer processing resources and data to computers and other devices on demand. Cloud computing is the hottest purpose built architecture created to support computer users. The cloud addresses three main areas of operation SaaS (software-as-a-service),PaaS (platform-as-a-service) and IaaS (infrastructure as a service). The large amount of data can be stored into cloud Data centers with low cost. We Integrate Data Mining and Cloud Computing to provide a quick access to this large volume amount of data on cloud. This paper aimed to study Clustering algorithm which can be applicable in cloud computing. This paper also describes the role of soft clustering in Cloud computing environment.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/32890776" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="b3d898931e94e49d3adca4aa29b3348a" rel="nofollow" data-download="{"attachment_id":53031493,"asset_id":32890776,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/53031493/download_file?st=MTczOTgwNDk1MSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="50335348" rel="nofollow" href="https://iiitb.academia.edu/IRJCSInternationalResearchJournalofComputerScience">IRJCS: : International Research Journal of Computer Science</a><script data-card-contents-for-user="50335348" type="text/json">{"id":50335348,"first_name":"IRJCS:","last_name":"International Research Journal of Computer Science","domain_name":"iiitb","page_name":"IRJCSInternationalResearchJournalofComputerScience","display_name":"IRJCS: : International Research Journal of Computer Science","profile_url":"https://iiitb.academia.edu/IRJCSInternationalResearchJournalofComputerScience?f_ri=103911","photo":"https://0.academia-photos.com/50335348/13242592/35131027/s65_irjcs_.international_research_journal_of_computer_science.jpg"}</script></span></span></li><li class="js-paper-rank-work_32890776 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="32890776"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 32890776, container: ".js-paper-rank-work_32890776", }); 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In This Tutorial You will be learned What is Xml, Xml file, Xml schema, features and Advantage of Xml.","downloadable_attachments":[{"id":36107683,"asset_id":9960560,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":14660782,"first_name":"Tpoint","last_name":"Tech","domain_name":"independent","page_name":"TpointTech","display_name":"Tpoint Tech","profile_url":"https://independent.academia.edu/TpointTech?f_ri=103911","photo":"https://0.academia-photos.com/14660782/3988162/145758186/s65_javatpoint..com.png"}],"research_interests":[{"id":5279,"name":"XML","url":"https://www.academia.edu/Documents/in/XML?f_ri=103911","nofollow":true},{"id":48713,"name":"XML Schema","url":"https://www.academia.edu/Documents/in/XML_Schema?f_ri=103911","nofollow":true},{"id":103911,"name":"XML data clustering","url":"https://www.academia.edu/Documents/in/XML_data_clustering?f_ri=103911","nofollow":true}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_35838838" data-work_id="35838838" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" rel="nofollow" href="https://www.academia.edu/35838838/SURVEY_OF_DATA_MINING_TECHNIQUES_USED_IN_HEALTHCARE_DOMAIN">SURVEY OF DATA MINING TECHNIQUES USED IN HEALTHCARE DOMAIN</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Health care industry produces enormous quantity of data that clutches complex information relating to patients and their medical conditions. Data mining is gaining popularity in different research arenas due to its infinite applications... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_35838838" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Health care industry produces enormous quantity of data that clutches complex information relating to patients and their medical conditions. Data mining is gaining popularity in different research arenas due to its infinite applications and methodologies to mine the information in correct manner. Data mining techniques have the capabilities to discover hidden patterns or relationships among the objects in the medical data. In last decade, there has been increase in usage of data mining techniques on medical data for determining useful trends or patterns that are used in analysis and decision making. Data mining has an infinite potential to utilize healthcare data more efficiently and effectually to predict different kind of disease. This paper features various Data Mining techniques such as classification, clustering, association and also highlights related work to analyse and predict human disease.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/35838838" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="1e592f5436c580c5d760afcefbbfe121" rel="nofollow" data-download="{"attachment_id":55716984,"asset_id":35838838,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/55716984/download_file?st=MTczOTgwNDk1MSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="74068792" rel="nofollow" href="https://independent.academia.edu/Journal_IJIST">International Journal of Information Sciences and Techniques (IJIST)</a><script data-card-contents-for-user="74068792" type="text/json">{"id":74068792,"first_name":"International Journal of Information Sciences and Techniques","last_name":"(IJIST)","domain_name":"independent","page_name":"Journal_IJIST","display_name":"International Journal of Information Sciences and Techniques (IJIST)","profile_url":"https://independent.academia.edu/Journal_IJIST?f_ri=103911","photo":"https://0.academia-photos.com/74068792/18783181/162964902/s65_international_journal_of_information_sciences_and_techniques._ijist_.png"}</script></span></span></li><li class="js-paper-rank-work_35838838 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="35838838"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 35838838, container: ".js-paper-rank-work_35838838", }); 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Data mining is gaining popularity in different research arenas due to its infinite applications and methodologies to mine the information in correct manner. Data mining techniques have the capabilities to discover hidden patterns or relationships among the objects in the medical data. In last decade, there has been increase in usage of data mining techniques on medical data for determining useful trends or patterns that are used in analysis and decision making. Data mining has an infinite potential to utilize healthcare data more efficiently and effectually to predict different kind of disease. This paper features various Data Mining techniques such as classification, clustering, association and also highlights related work to analyse and predict human disease.","downloadable_attachments":[{"id":55716984,"asset_id":35838838,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":74068792,"first_name":"International Journal of Information Sciences and Techniques","last_name":"(IJIST)","domain_name":"independent","page_name":"Journal_IJIST","display_name":"International Journal of Information Sciences and Techniques (IJIST)","profile_url":"https://independent.academia.edu/Journal_IJIST?f_ri=103911","photo":"https://0.academia-photos.com/74068792/18783181/162964902/s65_international_journal_of_information_sciences_and_techniques._ijist_.png"}],"research_interests":[{"id":465,"name":"Artificial Intelligence","url":"https://www.academia.edu/Documents/in/Artificial_Intelligence?f_ri=103911","nofollow":true},{"id":2009,"name":"Data 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href="https://www.academia.edu/2986178/XML_and_Semantic_Web_Technologies_Timeline_v1_3">XML and Semantic Web Technologies Timeline v1.3</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest">XML and Semantic Web Technologies Timeline v1.3 <br />[PDF File] <a href="http://bit.ly/TV64Jr" rel="nofollow">http://bit.ly/TV64Jr</a> <br />[SVG File] <a href="http://bitly.com/PkjzQr" rel="nofollow">http://bitly.com/PkjzQr</a> <br />[PNG File] <a href="http://bitly.com/Yl25ES" rel="nofollow">http://bitly.com/Yl25ES</a> <br /><br />(c) sparql2xquery 2016</div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/2986178" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" 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})();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_42874517" data-work_id="42874517" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/42874517/APPLIANCE_RECOGNITION_USING_A_DENSITY_BASED_CLUSTERING_APPROACH_WITH_MULTIPLE_GRANULARITIES">APPLIANCE RECOGNITION USING A DENSITY- BASED CLUSTERING APPROACH WITH MULTIPLE GRANULARITIES</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Electricity may not be economically stored as other forms of energy such that it would be in short supply during the peak time. In view of this difficulty, most power suppliers encourage their customers to adopt time-of-use rate plans.... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_42874517" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Electricity may not be economically stored as other forms of energy such that it would be in short supply during the peak time. In view of this difficulty, most power suppliers encourage their customers to adopt time-of-use rate plans. Consequently, it is essential for a user to be able to perceive the real-time information of power consumption. With the advancement of Internet of Things technologies, smart sockets are becoming a commodity to manage power consumption in a household. However, current smart sockets merely present the total electricity consumption rather than the individual consumption of household appliances. In this work, we thus design the capability of appliance recognition and implement this feature into a smart socket so that we can identify the power consumption of each appliance respectively. The proposed recursive DBSCAN approach realizes the recognition task without prior knowledge of new appliances and shows a feasible result in our experimental studies.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/42874517" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="45c125bbafda022e52a2fa706071e3c2" rel="nofollow" data-download="{"attachment_id":63116289,"asset_id":42874517,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/63116289/download_file?st=MTczOTgwNDk1MSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="15689241" href="https://independent.academia.edu/ComputerScienceInformationTechnologyCSIT">Computer Science & Information Technology (CS & IT) Computer Science Conference Proceedings (CSCP)</a><script data-card-contents-for-user="15689241" type="text/json">{"id":15689241,"first_name":"Computer Science \u0026 Information Technology (CS \u0026 IT)","last_name":"Computer Science Conference Proceedings (CSCP)","domain_name":"independent","page_name":"ComputerScienceInformationTechnologyCSIT","display_name":"Computer Science \u0026 Information Technology (CS \u0026 IT) Computer Science Conference Proceedings (CSCP)","profile_url":"https://independent.academia.edu/ComputerScienceInformationTechnologyCSIT?f_ri=103911","photo":"https://0.academia-photos.com/15689241/4239141/4932687/s65_computer_science_information_technology_cs_it_.computer_science_conference_proceedings_cscp_.jpg"}</script></span></span></li><li class="js-paper-rank-work_42874517 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="42874517"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 42874517, container: ".js-paper-rank-work_42874517", }); 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$(".js-view-count[data-work-id=42874517]").text(description); $(".js-view-count-work_42874517").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_42874517").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="42874517"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">3</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="103911" rel="nofollow" href="https://www.academia.edu/Documents/in/XML_data_clustering">XML data clustering</a>, <script data-card-contents-for-ri="103911" type="text/json">{"id":103911,"name":"XML data clustering","url":"https://www.academia.edu/Documents/in/XML_data_clustering?f_ri=103911","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="199424" rel="nofollow" href="https://www.academia.edu/Documents/in/Data_Clustering">Data Clustering</a>, <script data-card-contents-for-ri="199424" type="text/json">{"id":199424,"name":"Data Clustering","url":"https://www.academia.edu/Documents/in/Data_Clustering?f_ri=103911","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="307922" rel="nofollow" href="https://www.academia.edu/Documents/in/Data_Clustering_algorithms">Data Clustering algorithms</a><script data-card-contents-for-ri="307922" type="text/json">{"id":307922,"name":"Data Clustering algorithms","url":"https://www.academia.edu/Documents/in/Data_Clustering_algorithms?f_ri=103911","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=42874517]'), work: {"id":42874517,"title":"APPLIANCE RECOGNITION USING A DENSITY- BASED CLUSTERING APPROACH WITH MULTIPLE GRANULARITIES","created_at":"2020-04-27T22:39:51.018-07:00","url":"https://www.academia.edu/42874517/APPLIANCE_RECOGNITION_USING_A_DENSITY_BASED_CLUSTERING_APPROACH_WITH_MULTIPLE_GRANULARITIES?f_ri=103911","dom_id":"work_42874517","summary":"Electricity may not be economically stored as other forms of energy such that it would be in short supply during the peak time. In view of this difficulty, most power suppliers encourage their customers to adopt time-of-use rate plans. Consequently, it is essential for a user to be able to perceive the real-time information of power consumption. With the advancement of Internet of Things technologies, smart sockets are becoming a commodity to manage power consumption in a household. However, current smart sockets merely present the total electricity consumption rather than the individual consumption of household appliances. In this work, we thus design the capability of appliance recognition and implement this feature into a smart socket so that we can identify the power consumption of each appliance respectively. 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In this case... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_27416514" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">— In searching process user enter particular candidate searching keyword and with the help of searching algorithm respective searching query is executed on targeted dataset and result is return as an output of that algorithm. In this case it is expected that meaningful keyword has to be entered by user to get appropriate result set. In case of confusing bunch of keywords or ambiguity in it or short and indistinctness in it causes an irrelevant searching result. Also searching algorithms works on exact result fetching which can be irrelevant in case problem in input query and keyword. This problem statement is focused in this system. By considering the keyword and its relevant context in XML data , searching should be done using automatically diversification process of XML keyword search. In this way system may satisfy user, as user gets the analytical result set based on context of searching keywords. For more efficiency and to deal with big data, HADOOP platform is used. baseline efficient algorithms are proposed to incrementally compute top-k qualified query candidates as the diversified search intentions. Compare selection criteria are targeted: the k selected query candidates are most relevant to the given query while they have to cover maximal number of distinct results on real and synthetic data sets demonstrates the effectiveness diversification model and the efficiency of algorithms</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/27416514" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="38d84727383f9c64b901e6045a9b1804" rel="nofollow" data-download="{"attachment_id":47672579,"asset_id":27416514,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/47672579/download_file?st=MTczOTgwNDk1MSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="14545732" href="https://cvr.academia.edu/Ijcertpublications">IJCERT Publications</a><script data-card-contents-for-user="14545732" type="text/json">{"id":14545732,"first_name":"IJCERT","last_name":"Publications","domain_name":"cvr","page_name":"Ijcertpublications","display_name":"IJCERT Publications","profile_url":"https://cvr.academia.edu/Ijcertpublications?f_ri=103911","photo":"https://0.academia-photos.com/14545732/3964209/13239444/s65_ijcert.journal.jpg"}</script></span></span></li><li class="js-paper-rank-work_27416514 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="27416514"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 27416514, container: ".js-paper-rank-work_27416514", }); 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$(".js-view-count[data-work-id=27416514]").text(description); $(".js-view-count-work_27416514").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_27416514").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="27416514"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">5</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="2482" rel="nofollow" href="https://www.academia.edu/Documents/in/Database_Systems">Database Systems</a>, <script data-card-contents-for-ri="2482" type="text/json">{"id":2482,"name":"Database Systems","url":"https://www.academia.edu/Documents/in/Database_Systems?f_ri=103911","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="5279" rel="nofollow" href="https://www.academia.edu/Documents/in/XML">XML</a>, <script data-card-contents-for-ri="5279" type="text/json">{"id":5279,"name":"XML","url":"https://www.academia.edu/Documents/in/XML?f_ri=103911","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="94537" rel="nofollow" href="https://www.academia.edu/Documents/in/Datamining">Datamining</a>, <script data-card-contents-for-ri="94537" type="text/json">{"id":94537,"name":"Datamining","url":"https://www.academia.edu/Documents/in/Datamining?f_ri=103911","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="101060" rel="nofollow" href="https://www.academia.edu/Documents/in/XML_Database">XML Database</a><script data-card-contents-for-ri="101060" type="text/json">{"id":101060,"name":"XML Database","url":"https://www.academia.edu/Documents/in/XML_Database?f_ri=103911","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=27416514]'), work: {"id":27416514,"title":"Context Based XML Data and Diversification for Keyword Search Queries","created_at":"2016-07-31T09:46:28.595-07:00","url":"https://www.academia.edu/27416514/Context_Based_XML_Data_and_Diversification_for_Keyword_Search_Queries?f_ri=103911","dom_id":"work_27416514","summary":"— In searching process user enter particular candidate searching keyword and with the help of searching algorithm respective searching query is executed on targeted dataset and result is return as an output of that algorithm. In this case it is expected that meaningful keyword has to be entered by user to get appropriate result set. In case of confusing bunch of keywords or ambiguity in it or short and indistinctness in it causes an irrelevant searching result. Also searching algorithms works on exact result fetching which can be irrelevant in case problem in input query and keyword. This problem statement is focused in this system. By considering the keyword and its relevant context in XML data , searching should be done using automatically diversification process of XML keyword search. In this way system may satisfy user, as user gets the analytical result set based on context of searching keywords. For more efficiency and to deal with big data, HADOOP platform is used. baseline efficient algorithms are proposed to incrementally compute top-k qualified query candidates as the diversified search intentions. Compare selection criteria are targeted: the k selected query candidates are most relevant to the given query while they have to cover maximal number of distinct results on real and synthetic data sets demonstrates the effectiveness diversification model and the efficiency of algorithms","downloadable_attachments":[{"id":47672579,"asset_id":27416514,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":14545732,"first_name":"IJCERT","last_name":"Publications","domain_name":"cvr","page_name":"Ijcertpublications","display_name":"IJCERT Publications","profile_url":"https://cvr.academia.edu/Ijcertpublications?f_ri=103911","photo":"https://0.academia-photos.com/14545732/3964209/13239444/s65_ijcert.journal.jpg"}],"research_interests":[{"id":2482,"name":"Database Systems","url":"https://www.academia.edu/Documents/in/Database_Systems?f_ri=103911","nofollow":true},{"id":5279,"name":"XML","url":"https://www.academia.edu/Documents/in/XML?f_ri=103911","nofollow":true},{"id":94537,"name":"Datamining","url":"https://www.academia.edu/Documents/in/Datamining?f_ri=103911","nofollow":true},{"id":101060,"name":"XML Database","url":"https://www.academia.edu/Documents/in/XML_Database?f_ri=103911","nofollow":true},{"id":103911,"name":"XML data clustering","url":"https://www.academia.edu/Documents/in/XML_data_clustering?f_ri=103911"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_483263" data-work_id="483263" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/483263/On_data_mining_Tree_structured_data_represented_in_XML">On data mining Tree structured data represented in XML</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">The ubiquitous nature of XML as a data interchange format and the arrival of XMLdatabases offers new opportunities and challenges to data mining. The XML format permits the representation of data structures that cannot be easily mapped... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_483263" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">The ubiquitous nature of XML as a data interchange format and the arrival of XMLdatabases offers new opportunities and challenges to data mining. The XML format permits the <br />representation of data structures that cannot be easily mapped into a relational framework. New opportunities exist to mine relationships expressed by tree position and the presence or absence of sub trees as well as the conventional categorical and numeric data values contained at leaf nodes. Thispaper will consider why most conventional data mining algorithms are ill suited to mining tree data, <br />describe a novel XML based knowledge representation methodology called Metarule, and describe algorithms that do perform well. Two examples drawn from business will be used to demonstrate the application of these novel techniques. <br />Keywords: Data-mining, XML, Unstructured data</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/483263" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="42569f3e23ca54d1e2a8eacfe94a8d9f" rel="nofollow" data-download="{"attachment_id":2178266,"asset_id":483263,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/2178266/download_file?st=MTczOTgwNDk1MSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="355557" href="https://independent.academia.edu/AndrewEdmonds">Andrew Edmonds</a><script data-card-contents-for-user="355557" type="text/json">{"id":355557,"first_name":"Andrew","last_name":"Edmonds","domain_name":"independent","page_name":"AndrewEdmonds","display_name":"Andrew Edmonds","profile_url":"https://independent.academia.edu/AndrewEdmonds?f_ri=103911","photo":"https://0.academia-photos.com/355557/95211/107568/s65_andrew.edmonds.jpg"}</script></span></span></li><li class="js-paper-rank-work_483263 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="483263"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 483263, container: ".js-paper-rank-work_483263", }); });</script></li><li class="js-percentile-work_483263 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 483263; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_483263"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_483263 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="483263"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 483263; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=483263]").text(description); $(".js-view-count-work_483263").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_483263").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="483263"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">4</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="2009" rel="nofollow" href="https://www.academia.edu/Documents/in/Data_Mining">Data Mining</a>, <script data-card-contents-for-ri="2009" type="text/json">{"id":2009,"name":"Data Mining","url":"https://www.academia.edu/Documents/in/Data_Mining?f_ri=103911","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="103911" rel="nofollow" href="https://www.academia.edu/Documents/in/XML_data_clustering">XML data clustering</a>, <script data-card-contents-for-ri="103911" type="text/json">{"id":103911,"name":"XML data clustering","url":"https://www.academia.edu/Documents/in/XML_data_clustering?f_ri=103911","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="106355" rel="nofollow" href="https://www.academia.edu/Documents/in/Semi-structured_data_mining">Semi-structured data mining</a>, <script data-card-contents-for-ri="106355" type="text/json">{"id":106355,"name":"Semi-structured data mining","url":"https://www.academia.edu/Documents/in/Semi-structured_data_mining?f_ri=103911","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="106356" rel="nofollow" href="https://www.academia.edu/Documents/in/Xml_Data_mining">Xml Data mining</a><script data-card-contents-for-ri="106356" type="text/json">{"id":106356,"name":"Xml Data mining","url":"https://www.academia.edu/Documents/in/Xml_Data_mining?f_ri=103911","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=483263]'), work: {"id":483263,"title":"On data mining Tree structured data represented in XML","created_at":"2011-03-20T21:15:33.283-07:00","url":"https://www.academia.edu/483263/On_data_mining_Tree_structured_data_represented_in_XML?f_ri=103911","dom_id":"work_483263","summary":"The ubiquitous nature of XML as a data interchange format and the arrival of XMLdatabases offers new opportunities and challenges to data mining. The XML format permits the\r\nrepresentation of data structures that cannot be easily mapped into a relational framework. New opportunities exist to mine relationships expressed by tree position and the presence or absence of sub trees as well as the conventional categorical and numeric data values contained at leaf nodes. Thispaper will consider why most conventional data mining algorithms are ill suited to mining tree data,\r\ndescribe a novel XML based knowledge representation methodology called Metarule, and describe algorithms that do perform well. Two examples drawn from business will be used to demonstrate the application of these novel techniques.\r\nKeywords: Data-mining, XML, Unstructured data","downloadable_attachments":[{"id":2178266,"asset_id":483263,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":355557,"first_name":"Andrew","last_name":"Edmonds","domain_name":"independent","page_name":"AndrewEdmonds","display_name":"Andrew Edmonds","profile_url":"https://independent.academia.edu/AndrewEdmonds?f_ri=103911","photo":"https://0.academia-photos.com/355557/95211/107568/s65_andrew.edmonds.jpg"}],"research_interests":[{"id":2009,"name":"Data Mining","url":"https://www.academia.edu/Documents/in/Data_Mining?f_ri=103911","nofollow":true},{"id":103911,"name":"XML data clustering","url":"https://www.academia.edu/Documents/in/XML_data_clustering?f_ri=103911","nofollow":true},{"id":106355,"name":"Semi-structured data mining","url":"https://www.academia.edu/Documents/in/Semi-structured_data_mining?f_ri=103911","nofollow":true},{"id":106356,"name":"Xml Data mining","url":"https://www.academia.edu/Documents/in/Xml_Data_mining?f_ri=103911","nofollow":true}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_25017478" data-work_id="25017478" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" rel="nofollow" href="https://www.academia.edu/25017478/DYNAMIC_K_MEANS_ALGORITHM_FOR_OPTIMIZED_ROUTING_IN_MOBILE_AD_HOC_NETWORKS">DYNAMIC K-MEANS ALGORITHM FOR OPTIMIZED ROUTING IN MOBILE AD HOC NETWORKS</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">In this paper, a dynamic K-means algorithm to improve the routing process in Mobile Ad-Hoc networks (MANETs) is presented. Mobile ad-hoc networks are a collocation of mobile wireless nodes that can operate without using focal access... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_25017478" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">In this paper, a dynamic K-means algorithm to improve the routing process in Mobile Ad-Hoc networks (MANETs) is presented. Mobile ad-hoc networks are a collocation of mobile wireless nodes that can operate without using focal access points, pre-existing infrastructures, or a centralized management point. In MANETs, the quick motion of nodes modifies the topology of network. This feature of MANETS is lead to various problems in the routing process such as increase of the overhead massages and inefficient routing between nodes of network. A large variety of clustering methods have been developed for establishing an efficient routing process in MANETs. Routing is one of the crucial topics which are having significant impact on MANETs performance. The K-means algorithm is one of the effective clustering methods aimed to reduce routing difficulties related to bandwidth, throughput and power consumption. This paper proposed a new K-means clustering algorithm to find out optimal path from source node to destinations node in MANETs. The main goal of proposed approach which is called the dynamic K-means clustering methods is to solve the limitation of basic K-means method like permanent cluster head and fixed cluster members. The experimental results demonstrate that using dynamic K-means scheme enhance the performance of routing process in Mobile ad-hoc networks.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/25017478" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="a630e8b51b5d4d911378394324e034e3" rel="nofollow" data-download="{"attachment_id":45341941,"asset_id":25017478,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/45341941/download_file?st=MTczOTgwNDk1MSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="26099471" rel="nofollow" href="https://independent.academia.edu/JournalIJCSES">International Journal of Computer Science and Engineering Survey (IJCSES)</a><script data-card-contents-for-user="26099471" type="text/json">{"id":26099471,"first_name":"International Journal of Computer Science and Engineering Survey","last_name":"(IJCSES)","domain_name":"independent","page_name":"JournalIJCSES","display_name":"International Journal of Computer Science and Engineering Survey (IJCSES)","profile_url":"https://independent.academia.edu/JournalIJCSES?f_ri=103911","photo":"https://0.academia-photos.com/26099471/8320289/35522477/s65_international_journal_of_computer_science_and_engineering_survey._ijcses_.png"}</script></span></span></li><li class="js-paper-rank-work_25017478 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="25017478"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 25017478, container: ".js-paper-rank-work_25017478", }); });</script></li><li class="js-percentile-work_25017478 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 25017478; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_25017478"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_25017478 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="25017478"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 25017478; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=25017478]").text(description); $(".js-view-count-work_25017478").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_25017478").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="25017478"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">18</a>  </div><span class="InlineList-item-text u-textTruncate u-pl10x"><a class="InlineList-item-text" data-has-card-for-ri="2339" rel="nofollow" href="https://www.academia.edu/Documents/in/Mobile_Ad_Hoc_Networks">Mobile Ad Hoc Networks</a>, <script data-card-contents-for-ri="2339" type="text/json">{"id":2339,"name":"Mobile Ad Hoc Networks","url":"https://www.academia.edu/Documents/in/Mobile_Ad_Hoc_Networks?f_ri=103911","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="5486" rel="nofollow" href="https://www.academia.edu/Documents/in/Clustering_and_Classification_Methods">Clustering and Classification Methods</a>, <script data-card-contents-for-ri="5486" type="text/json">{"id":5486,"name":"Clustering and Classification Methods","url":"https://www.academia.edu/Documents/in/Clustering_and_Classification_Methods?f_ri=103911","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="13143" rel="nofollow" href="https://www.academia.edu/Documents/in/Clustering_Algorithms">Clustering Algorithms</a>, <script data-card-contents-for-ri="13143" type="text/json">{"id":13143,"name":"Clustering Algorithms","url":"https://www.academia.edu/Documents/in/Clustering_Algorithms?f_ri=103911","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="14335" rel="nofollow" href="https://www.academia.edu/Documents/in/MANET_Routing_protocols">MANET Routing protocols</a><script data-card-contents-for-ri="14335" type="text/json">{"id":14335,"name":"MANET Routing protocols","url":"https://www.academia.edu/Documents/in/MANET_Routing_protocols?f_ri=103911","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=25017478]'), work: {"id":25017478,"title":"DYNAMIC K-MEANS ALGORITHM FOR OPTIMIZED ROUTING IN MOBILE AD HOC NETWORKS","created_at":"2016-05-04T04:01:22.230-07:00","url":"https://www.academia.edu/25017478/DYNAMIC_K_MEANS_ALGORITHM_FOR_OPTIMIZED_ROUTING_IN_MOBILE_AD_HOC_NETWORKS?f_ri=103911","dom_id":"work_25017478","summary":"In this paper, a dynamic K-means algorithm to improve the routing process in Mobile Ad-Hoc networks (MANETs) is presented. Mobile ad-hoc networks are a collocation of mobile wireless nodes that can operate without using focal access points, pre-existing infrastructures, or a centralized management point. In MANETs, the quick motion of nodes modifies the topology of network. This feature of MANETS is lead to various problems in the routing process such as increase of the overhead massages and inefficient routing between nodes of network. A large variety of clustering methods have been developed for establishing an efficient routing process in MANETs. Routing is one of the crucial topics which are having significant impact on MANETs performance. The K-means algorithm is one of the effective clustering methods aimed to reduce routing difficulties related to bandwidth, throughput and power consumption. This paper proposed a new K-means clustering algorithm to find out optimal path from source node to destinations node in MANETs. The main goal of proposed approach which is called the dynamic K-means clustering methods is to solve the limitation of basic K-means method like permanent cluster head and fixed cluster members. The experimental results demonstrate that using dynamic K-means scheme enhance the performance of routing process in Mobile ad-hoc networks.","downloadable_attachments":[{"id":45341941,"asset_id":25017478,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":26099471,"first_name":"International Journal of Computer Science and Engineering Survey","last_name":"(IJCSES)","domain_name":"independent","page_name":"JournalIJCSES","display_name":"International Journal of Computer Science and Engineering Survey (IJCSES)","profile_url":"https://independent.academia.edu/JournalIJCSES?f_ri=103911","photo":"https://0.academia-photos.com/26099471/8320289/35522477/s65_international_journal_of_computer_science_and_engineering_survey._ijcses_.png"}],"research_interests":[{"id":2339,"name":"Mobile Ad Hoc Networks","url":"https://www.academia.edu/Documents/in/Mobile_Ad_Hoc_Networks?f_ri=103911","nofollow":true},{"id":5486,"name":"Clustering and Classification Methods","url":"https://www.academia.edu/Documents/in/Clustering_and_Classification_Methods?f_ri=103911","nofollow":true},{"id":13143,"name":"Clustering Algorithms","url":"https://www.academia.edu/Documents/in/Clustering_Algorithms?f_ri=103911","nofollow":true},{"id":14335,"name":"MANET Routing protocols","url":"https://www.academia.edu/Documents/in/MANET_Routing_protocols?f_ri=103911","nofollow":true},{"id":16579,"name":"Route Optimization For Waste Transportation","url":"https://www.academia.edu/Documents/in/Route_Optimization_For_Waste_Transportation?f_ri=103911"},{"id":35825,"name":"Mobility Modeling (Mobile Ad Hoc Networks)","url":"https://www.academia.edu/Documents/in/Mobility_Modeling_Mobile_Ad_Hoc_Networks_?f_ri=103911"},{"id":82137,"name":"Manet","url":"https://www.academia.edu/Documents/in/Manet?f_ri=103911"},{"id":84990,"name":"Clustering","url":"https://www.academia.edu/Documents/in/Clustering?f_ri=103911"},{"id":89055,"name":"Route Optimization","url":"https://www.academia.edu/Documents/in/Route_Optimization?f_ri=103911"},{"id":103911,"name":"XML data clustering","url":"https://www.academia.edu/Documents/in/XML_data_clustering?f_ri=103911"},{"id":128576,"name":"Mobile Ad-hoc Networks (MANETs)","url":"https://www.academia.edu/Documents/in/Mobile_Ad-hoc_Networks_MANETs_?f_ri=103911"},{"id":163785,"name":"Graph Clustering","url":"https://www.academia.edu/Documents/in/Graph_Clustering?f_ri=103911"},{"id":293560,"name":"Clustering Algorithm in Manet","url":"https://www.academia.edu/Documents/in/Clustering_Algorithm_in_Manet?f_ri=103911"},{"id":311740,"name":"Clustering and routing of mobile ad hoc networks","url":"https://www.academia.edu/Documents/in/Clustering_and_routing_of_mobile_ad_hoc_networks?f_ri=103911"},{"id":568857,"name":"Mobile Ad-hoc Networks","url":"https://www.academia.edu/Documents/in/Mobile_Ad-hoc_Networks?f_ri=103911"},{"id":742006,"name":"Optimization of Collection Route( Solid Waste Management)","url":"https://www.academia.edu/Documents/in/Optimization_of_Collection_Route_Solid_Waste_Management_?f_ri=103911"},{"id":914128,"name":"Fuzzy C-Means Clustering Algorithm","url":"https://www.academia.edu/Documents/in/Fuzzy_C-Means_Clustering_Algorithm?f_ri=103911"},{"id":1032324,"name":"K means Clustering","url":"https://www.academia.edu/Documents/in/K_means_Clustering?f_ri=103911"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_10347901" data-work_id="10347901" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" rel="nofollow" href="https://www.academia.edu/10347901/METHODOLOGICAL_STUDY_OF_OPINION_MINING_AND_SENTIMENT_ANALYSIS_TECHNIQUES">METHODOLOGICAL STUDY OF OPINION MINING AND SENTIMENT ANALYSIS TECHNIQUES</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Decision making both on individual and organizational level is always accompanied by the search of other’s opinion on the same. With tremendous establishment of opinion rich resources like, reviews, forum discussions, blogs, micro-blogs,... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_10347901" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Decision making both on individual and organizational level is always accompanied by the search of other’s opinion on the same. With tremendous establishment of opinion rich resources like, reviews, forum discussions, blogs, micro-blogs, Twitter etc provide a rich anthology of sentiments. This user generated content can serve as a benefaction to market if the semantic orientations are deliberated. Opinion mining and sentiment analysis are the formalization for studying and construing opinions and sentiments. The digital ecosystem has itself paved way for use of huge volume of opinionated data recorded. This paper is an attempt to review and evaluate the various techniques used for opinion and sentiment analysis.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/10347901" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="f171c28ba505e0cf20ca5ed370723617" rel="nofollow" data-download="{"attachment_id":36412169,"asset_id":10347901,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/36412169/download_file?st=MTczOTgwNDk1MSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="22251637" rel="nofollow" href="https://independent.academia.edu/IjscJournal">International Journal on Soft Computing ( IJSC )</a><script data-card-contents-for-user="22251637" type="text/json">{"id":22251637,"first_name":"International Journal on Soft Computing","last_name":"( IJSC )","domain_name":"independent","page_name":"IjscJournal","display_name":"International Journal on Soft Computing ( IJSC )","profile_url":"https://independent.academia.edu/IjscJournal?f_ri=103911","photo":"https://0.academia-photos.com/22251637/6206970/119551446/s65_international_journal_on_soft_computing._ijsc_.png"}</script></span></span></li><li class="js-paper-rank-work_10347901 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="10347901"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 10347901, container: ".js-paper-rank-work_10347901", }); 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