Colocation Mining In Uncertain Data Sets : A Probablistic Approach

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Uncertain data is a partially complete data. Many of the real world data is Uncertain, for example, Demographic data, Sensor networks data, GIS data etc.,. Handling such data is a challenge for knowledge discovery particularly in colocation mining. One straightforward method is to find the Probabilistic Prevalent colocations (PPCs). This method tries to find all colocations that are to be generated from a random world. For this we first apply an approximation error to find all the PPCs which reduce the computations. Next find all the possible worlds and split them into two different worlds and compute the prevalence probability. These worlds are used to compare with a minimum probability threshold to decide whether it is Probabilistic Prevalent colocation (PPCs) or not. The experimental results on the selected data set show the significant improvement in computational time in comparison to some of the existing methods used in colocation mining. "/> <meta name="keywords" content="Probabilistic Approach, Colocation Mining, Un-certain Data Sets"/>   <meta name="dc.title" content="Colocation Mining In Uncertain Data Sets : A Probablistic Approach"> <meta name="citation_author" content="M.Sheshikala"> <meta name="citation_author" content="D. Rajeswara Rao"> <meta name="citation_author" content="Md. Ali Kadampur"> <meta name="dc.type" content="Article"> <meta name="dc.source" content="International Journal on Cybernetics & Informatics (IJCI),Vol. 5, No. 1"> <meta name="dc.date" content="2016/2/28"> <meta name="dc.identifier" content=" 10.5121/ijci.2016.5101"> <meta name="dc.publisher" content="AIRCC Publishing Corporation"> <meta name="dc.rights" content="http://creativecommons.org/licenses/by/3.0/"> <meta name="dc.format" content="application/pdf"> <meta name="dc.language" content="en"> <meta name="dc.description" content="In this paper we investigate colocation mining problem in the context of uncertain data. Uncertain data is a partially complete data. Many of the real world data is Uncertain, for example, Demographic data, Sensor networks data, GIS data etc.,. Handling such data is a challenge for knowledge discovery particularly in colocation mining. One straightforward method is to find the Probabilistic Prevalent colocations (PPCs). This method tries to find all colocations that are to be generated from a random world. For this we first apply an approximation error to find all the PPCs which reduce the computations. Next find all the possible worlds and split them into two different worlds and compute the prevalence probability. These worlds are used to compare with a minimum probability threshold to decide whether it is Probabilistic Prevalent colocation (PPCs) or not. The experimental results on the selected data set show the significant improvement in computational time in comparison to some of the existing methods used in colocation mining."/> <meta name="dc.subject" content="Probabilistic Approach"> <meta name="dc.subject" content="Colocation Mining"> <meta name="dc.subject" content="Un-certain Data Sets">   <meta name="prism.publicationName" content="International Journal on Cybernetics & Informatics (IJCI),Vol. 5, No. 1"> <meta name="prism.publicationDate" content="2016/2/28"> <meta name="prism.volume" content="5"> <meta name="prism.number" content="1"> <meta name="prism.section" content="Article"> <meta name="prism.startingPage" content="1">   <meta name="citation_journal_title" content="International Journal on Cybernetics & Informatics (IJCI),Vol. 5, No. 1"> <meta name="citation_publisher" content="AIRCC Publishing Corporation"> <meta name="citation_author" content="M.Sheshikala"> <meta name="citation_author" content="D. Rajeswara Rao"> <meta name="citation_author" content="Md. Ali Kadampur"> <meta name="citation_title" content="Colocation Mining In Uncertain Data Sets : A Probablistic Approach"> <meta name="citation_online_date" content="2016/2/28"> <meta name="citation_volume" content="5"> <meta name="citation_issue" content="1"> <meta name="citation_firstpage" content="1"> <meta name="citation_author" content="M.Sheshikala"> <meta name="citation_author" content="D. Rajeswara Rao"> <meta name="citation_author" content="Md. Ali Kadampur"> <meta name="citation_doi" content=" 10.5121/ijci.2016.5101"> <meta name="citation_abstract_html_url" content="https://ijcionline.com/abstract/5116ijci01"> <meta name="citation_pdf_url" content="https://aircconline.com/ijci/V5N1/5116ijci01.pdf">   <meta property="og:site_name" content="AIRCC" /> <meta property="og:type" content="article" /> <meta property="og:url" content="https://ijcionline.com/abstract/5116ijci01"/> <meta property="og:title" content="Colocation Mining In Uncertain Data Sets : A Probablistic Approach"> <meta property="og:description" content="In this paper we investigate colocation mining problem in the context of uncertain data. Uncertain data is a partially complete data. Many of the real world data is Uncertain, for example, Demographic data, Sensor networks data, GIS data etc.,. Handling such data is a challenge for knowledge discovery particularly in colocation mining. One straightforward method is to find the Probabilistic Prevalent colocations (PPCs). This method tries to find all colocations that are to be generated from a random world. For this we first apply an approximation error to find all the PPCs which reduce the computations. Next find all the possible worlds and split them into two different worlds and compute the prevalence probability. These worlds are used to compare with a minimum probability threshold to decide whether it is Probabilistic Prevalent colocation (PPCs) or not. The experimental results on the selected data set show the significant improvement in computational time in comparison to some of the existing methods used in colocation mining."/>   <meta name="google-site-verification" content="t8rHIcM8EfjIqfQzQ0IdYIiA9JxDD0uUZAitBCzsOIw" /> <meta name="yandex-verification" content="e3d2d5a32c7241f4" />   </head> <body>  <div class="navbar-fixed"> <nav class="cyan lighten-2 z-depth-5"> <div class="container"> <div class="nav-wrapper"> <ul> <li id="b-logo"> <img id="brand-logo" href="/index" class="hide-on-med-and-down" src="/img/aircc-logo1.jpg" width="60" height="60" style="vertical-align:middle"> </li> </ul> <a class="brand-logo " href="/index"><h5>IJCI <span class="hide-on-med-and-down">Conference Proceedings</span></h5></a> <a data-activates="side-nav" class="button-collapse show-on-small left"> <i class="material-icons">menu</i> </a> <ul class="right hide-on-med-and-down"> <li class=""> <a href="/index">Home</a> </li> <li class=""> <a href="/volume13">Current issue</a> </li> <li> <a href="/archives">Archives</a> </li> <li class=""> <a href="/contact">Contact</a> </li> </ul> </div> </div> </nav> </div>  <ul class="side-nav" id="side-nav"> <li> <div class="user-view arc"> <div class="background"> <img class="mobile-overlay" > </div> <a href="/index"> <i id="cl" class="material-icons cyan-text text-lighten-2 right">close</i> </a> <a href="/index"> <img class="circle" src="/img/aircc-logo1.jpg"> </a> <h5 class="">IJCI Conference Proceedings</h5> </div> </li> <li class=""> <a href="/index">Home <i class="fas fa-user"></i> </a> </li> <li class=""> <a href="/volume13">Current issue <i class="fas fa-user"></i> </a> </li> <li> <a href="/archives">Archives <i class="fas fa-user"></i> </a> </li> <li class=""> <a href="/contact">Contact <i class="fas fa-user"></i> </a> </li> </ul> <div class="fixed-action-btn" id="scrollTop"> <a class="btn btn-small btn-floating waves-effect waves-light blue lighten-1 pulse" onclick="topFunction()"> <i class="material-icons">keyboard_arrow_up</i> </a> </div>  <section class="section-main" > <div class="container"> <div class="row"> <div class="col s12 m9">  <div class="card z-depth-2"> <div class="card-content"> <h5 class="cyan-text center text-darken-1"> COLOCATION MINING IN UNCERTAIN DATA SETS:A PROBABILISTIC APPROACH </h5> </div> </div> <br> <div class="card"> <h5 id="about" class="brown-text text-darken-2 text-center" style="padding-bottom:0px">Authors</h5>  <div class="card-content"> <p class="left-text" style="text-align:justify"> M.Sheshikala<sup>1</sup>, D. Rajeswara Rao<sup>2</sup>, and Md. Ali Kadampur<sup>3</sup> <br><sup>1</sup>,<sup>3</sup>S.R Engineering College<sup>2</sup>Kl University</br> </p> </div> </div>   <div class="card"> <h5 id="about" class="brown-text text-darken-2 text-center" style="padding-bottom:0px">Abstract</h5>  <div class="card-content"> <p class="left-text" style="text-align:justify"> In this paper we investigate colocation mining problem in the context of uncertain data. Uncertain data is a partially complete data. Many of the real world data is Uncertain, for example, Demographic data, Sensor networks data, GIS data etc.,. Handling such data is a challenge for knowledge discovery particularly in colocation mining. One straightforward method is to find the Probabilistic Prevalent colocations (PPCs). This method tries to find all colocations that are to be generated from a random world. For this we first apply an approximation error to find all the PPCs which reduce the computations. Next find all the possible worlds and split them into two different worlds and compute the prevalence probability. These worlds are used to compare with a minimum probability threshold to decide whether it is Probabilistic Prevalent colocation (PPCs) or not. The experimental results on the selected data set show the significant improvement in computational time in comparison to some of the existing methods used in colocation mining. </p> </div> </div> <div class="card"> <h5 id="about" class="brown-text text-darken-2 text-center" style="padding-bottom:0px">Keywords</h5>  <div class="card-content"> <p class="left-text" style="text-align:justify"> Probabilistic Approach, Colocation Mining, Un-certain Data Sets</p></div> </div> <div class="card-content"> <a href="https://aircconline.com/ijci/V5N1/5116ijci01.pdf" target="blank" class="btn btn-small lighten-2 cyan lig">Full Text</a>  <a href="https://ijcionline.com/vol5" class="btn btn-small lighten-2 cyan lig">Volume 5</a> </div> </div>  <div id="side-bar" class="col s12 m3"> <div id="section-main"> <br> <br> <div class="card side cyan lighten-2"> <div class="card-content"> <ul> <li class="ax waves-effect waves-light"> <a class="white-text" href="/editorial" > <i class="material-icons left">account_circle</i>Editorial Board</a> <br> </li> <br> <br> <div class="divider"></div> <br> <li class="ax waves-effect waves-light"> <a class="white-text" href="/mostcitedarticels" > <i class="material-icons left">book</i>Most Cited Articels </a> <br> </li> <br> <br> <div class="divider"></div> <br> <li class="ax waves-effect waves-light"> <a class="white-text" href="/indexing" > <i class="material-icons left">list</i>Indexing </a> <br> </li> <br> <br> <div class="divider"></div> <br> <li class="ax waves-effect waves-light"> <a class="white-text" href="/faq" > <i class="material-icons left">help</i>FAQ </a> <br> </li> <br> <br> <div class="divider"></div> <br> </div> </div> </div> </div> </div> </div> </div> </section> <br> <br> <br> <br> <div id="txtcnt"></div>  <footer class="page-footer cyan lighten-3"> <div class="nav-wrapper"> <div class="container"> <ul> <li> <a target="_blank" href="http://airccse.org/"> <img src="/img/since2008.png" alt="since2008"></a> </li> </ul> <h6> Free Open Access Conference Proceedings <br> Computer Science & Engineering - Information Technology - Information Systems</h6> </div> <div class="footer-m col m3 s12 offset-m1"> </div> <div class="social col m3 offset-m1 s12"> </div> </div> </div> <div class="col s12 m10 offset-m1"> <div class="grey darken-3 center-align"> <small class="white-text">Designed and Developed by NNN Team</small> </div> </div> </footer> </body>  <script type="text/javascript" src="https://code.jquery.com/jquery-3.2.1.min.js"></script> <script type="text/javascript" src="/js/materialize.min.js"></script> <script src="/js/search.js"></script> <script src="/js/scrolltop.js"></script> <script src="/js/popup.js"></script> <script src="/js/main.jquery.js"></script> </html>

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Colocation Mining In Uncertain Data Sets : A Probablistic Approach