Search results for: P. Ranjetha

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Ranjetha"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> <button class="d-block d-lg-none navbar-toggler ml-auto" type="button" data-toggle="collapse" data-target="#navbarMenu" aria-controls="navbarMenu" aria-expanded="false" aria-label="Toggle navigation"> <span class="navbar-toggler-icon"></span> </button> <div class="w-100"> <div class="d-none d-lg-flex flex-row-reverse"> <form method="get" action="https://waset.org/search" class="form-inline my-2 my-lg-0"> <input class="form-control mr-sm-2" type="search" placeholder="Search Conferences" value="P. 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Ranjetha"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 4</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: P. Ranjetha</h1> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4</span> STATISTICA Software: A State of the Art Review</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=S.%20Sarumathi">S. Sarumathi</a>, <a href="https://publications.waset.org/search?q=N.%20Shanthi"> N. Shanthi</a>, <a href="https://publications.waset.org/search?q=S.%20Vidhya"> S. Vidhya</a>, <a href="https://publications.waset.org/search?q=P.%20Ranjetha"> P. Ranjetha</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>Data mining idea is mounting rapidly in admiration and also in their popularity. The foremost aspire of data mining method is to extract data from a huge data set into several forms that could be comprehended for additional use. The data mining is a technology that contains with rich potential resources which could be supportive for industries and businesses that pay attention to collect the necessary information of the data to discover their customer’s performances. For extracting data there are several methods are available such as Classification, Clustering, Association, Discovering, and Visualization… etc., which has its individual and diverse algorithms towards the effort to fit an appropriate model to the data. STATISTICA mostly deals with excessive groups of data that imposes vast rigorous computational constraints. These results trials challenge cause the emergence of powerful STATISTICA Data Mining technologies. In this survey an overview of the STATISTICA software is illustrated along with their significant features.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Data%20Mining" title="Data Mining">Data Mining</a>, <a href="https://publications.waset.org/search?q=STATISTICA%20Data%20Miner" title=" STATISTICA Data Miner"> STATISTICA Data Miner</a>, <a href="https://publications.waset.org/search?q=Text%0D%0AMiner" title=" Text Miner"> Text Miner</a>, <a href="https://publications.waset.org/search?q=Enterprise%20Server" title=" Enterprise Server"> Enterprise Server</a>, <a href="https://publications.waset.org/search?q=Classification" title=" Classification"> Classification</a>, <a href="https://publications.waset.org/search?q=Association" title=" Association"> Association</a>, <a href="https://publications.waset.org/search?q=Clustering" title=" Clustering"> Clustering</a>, <a href="https://publications.waset.org/search?q=Regression." title=" Regression."> Regression.</a> </p> <a href="https://publications.waset.org/10001057/statistica-software-a-state-of-the-art-review" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10001057/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/10001057/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/10001057/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/10001057/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/10001057/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/10001057/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/10001057/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/10001057/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/10001057/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/10001057/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/10001057.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">2607</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3</span> Analysis of Diverse Cluster Ensemble Techniques</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=S.%20Sarumathi">S. Sarumathi</a>, <a href="https://publications.waset.org/search?q=N.%20Shanthi"> N. Shanthi</a>, <a href="https://publications.waset.org/search?q=P.%20Ranjetha"> P. Ranjetha</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Data mining is the procedure of determining interesting patterns from the huge amount of data. With the intention of accessing the data faster the most supporting processes needed is clustering. Clustering is the process of identifying similarity between data according to the individuality present in the data and grouping associated data objects into clusters. Cluster ensemble is the technique to combine various runs of different clustering algorithms to obtain a general partition of the original dataset, aiming for consolidation of outcomes from a collection of individual clustering outcomes. The performances of clustering ensembles are mainly affecting by two principal factors such as diversity and quality. This paper presents the overview about the different cluster ensemble algorithm along with their methods used in cluster ensemble to improve the diversity and quality in the several cluster ensemble related papers and shows the comparative analysis of different cluster ensemble also summarize various cluster ensemble methods. Henceforth this clear analysis will be very useful for the world of clustering experts and also helps in deciding the most appropriate one to determine the problem in hand. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Cluster%20Ensemble" title="Cluster Ensemble">Cluster Ensemble</a>, <a href="https://publications.waset.org/search?q=Consensus%20Function" title=" Consensus Function"> Consensus Function</a>, <a href="https://publications.waset.org/search?q=CSPA" title=" CSPA"> CSPA</a>, <a href="https://publications.waset.org/search?q=Diversity" title=" Diversity"> Diversity</a>, <a href="https://publications.waset.org/search?q=HGPA" title=" HGPA"> HGPA</a>, <a href="https://publications.waset.org/search?q=MCLA." title=" MCLA."> MCLA.</a> </p> <a href="https://publications.waset.org/10003748/analysis-of-diverse-cluster-ensemble-techniques" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10003748/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/10003748/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/10003748/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/10003748/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/10003748/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/10003748/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/10003748/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/10003748/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/10003748/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/10003748/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/10003748.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1841</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2</span> Comparative Analysis of Machine Learning Tools: A Review</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=S.%20Sarumathi">S. Sarumathi</a>, <a href="https://publications.waset.org/search?q=M.%20Vaishnavi"> M. Vaishnavi</a>, <a href="https://publications.waset.org/search?q=S.%20Geetha"> S. Geetha</a>, <a href="https://publications.waset.org/search?q=P.%20Ranjetha"> P. Ranjetha</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Machine learning is a new and exciting area of artificial intelligence nowadays. Machine learning is the most valuable, time, supervised, and cost-effective approach. It is not a narrow learning approach; it also includes a wide range of methods and techniques that can be applied to a wide range of complex realworld problems and time domains. Biological image classification, adaptive testing, computer vision, natural language processing, object detection, cancer detection, face recognition, handwriting recognition, speech recognition, and many other applications of machine learning are widely used in research, industry, and government. Every day, more data are generated, and conventional machine learning techniques are becoming obsolete as users move to distributed and real-time operations. By providing fundamental knowledge of machine learning tools and research opportunities in the field, the aim of this article is to serve as both a comprehensive overview and a guide. A diverse set of machine learning resources is demonstrated and contrasted with the key features in this survey. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Artificial%20intelligence" title="Artificial intelligence">Artificial intelligence</a>, <a href="https://publications.waset.org/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/search?q=deep%0D%0Alearning" title=" deep learning"> deep learning</a>, <a href="https://publications.waset.org/search?q=machine%20learning%20algorithms" title=" machine learning algorithms"> machine learning algorithms</a>, <a href="https://publications.waset.org/search?q=machine%20learning%20tools." title=" machine learning tools."> machine learning tools.</a> </p> <a href="https://publications.waset.org/10012071/comparative-analysis-of-machine-learning-tools-a-review" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10012071/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/10012071/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/10012071/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/10012071/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/10012071/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/10012071/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/10012071/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/10012071/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/10012071/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/10012071/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/10012071.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1850</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1</span> A Review and Comparative Analysis on Cluster Ensemble Methods</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=S.%20Sarumathi">S. Sarumathi</a>, <a href="https://publications.waset.org/search?q=P.%20Ranjetha"> P. Ranjetha</a>, <a href="https://publications.waset.org/search?q=C.%20Saraswathy"> C. Saraswathy</a>, <a href="https://publications.waset.org/search?q=M.%20Vaishnavi"> M. Vaishnavi</a>, <a href="https://publications.waset.org/search?q=S.%20Geetha"> S. Geetha</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>Clustering is an unsupervised learning technique for aggregating data objects into meaningful classes so that intra cluster similarity is maximized and inter cluster similarity is minimized in data mining. However, no single clustering algorithm proves to be the most effective in producing the best result. As a result, a new challenging technique known as the cluster ensemble approach has blossomed in order to determine the solution to this problem. For the cluster analysis issue, this new technique is a successful approach. The cluster ensemble's main goal is to combine similar clustering solutions in a way that achieves the precision while also improving the quality of individual data clustering. Because of the massive and rapid creation of new approaches in the field of data mining, the ongoing interest in inventing novel algorithms necessitates a thorough examination of current techniques and future innovation. This paper presents a comparative analysis of various cluster ensemble approaches, including their methodologies, formal working process, and standard accuracy and error rates. As a result, the society of clustering practitioners will benefit from this exploratory and clear research, which will aid in determining the most appropriate solution to the problem at hand.</p> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Clustering" title="Clustering">Clustering</a>, <a href="https://publications.waset.org/search?q=cluster%20ensemble%20methods" title=" cluster ensemble methods"> cluster ensemble methods</a>, <a href="https://publications.waset.org/search?q=consensus%20function" title=" consensus function"> consensus function</a>, <a href="https://publications.waset.org/search?q=data%20mining" title=" data mining"> data mining</a>, <a href="https://publications.waset.org/search?q=unsupervised%20learning." title=" unsupervised learning. 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Search results for: P. Ranjetha