Extracting the Significant Degrees of Attributes in Unlabeled Data using Unsupervised Machine Learning

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These significant degrees are applied to the original dataset and generate the weighted dataset reflected by the degrees of influentialvalues for the set ofattributes. This work is simulated on the UCI Machine Learning repository dataset. The Scikit-learn K-Means clustering with raw data, scaled data, and the weighted data are tested. The result shows that the proposed approach improves the performance"/> <meta name="keywords" content="Unsupervised MachineLearning, Simple Competitive Learning, SignificantDegree of Attributes, Scikitlearn K-Means Clustering, Weighted Data, UCI Machine Learning Data"/>   <meta name="dc.title" content="Extracting the Significant Degrees of Attributes in Unlabeled Data using Unsupervised Machine Learning"> <meta name="citation_author" content="Byoung Jik Lee"> <meta name="dc.type" content="Article"> <meta name="dc.source" content="Computer Science & Information Technology (CS & IT), Vol 10, No.16"> <meta name="dc.date" content="28-11-2020"> <meta name="dc.identifier" content="10.5121/csit.2020.101608"> <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="We propose a valid approach to find the degree of important attributes in unlabeled dataset to improve the clustering performance. The significant degrees of attributes are extracted through the training of unsupervised simple competitive learning with the raw unlabeled data. These significant degrees are applied to the original dataset and generate the weighted dataset reflected by the degrees of influentialvalues for the set ofattributes. This work is simulated on the UCI Machine Learning repository dataset. The Scikit-learn K-Means clustering with raw data, scaled data, and the weighted data are tested. The result shows that the proposed approach improves the performance"/> <meta name="dc.subject" content="Unsupervised MachineLearning"> <meta name="dc.subject" content="Simple Competitive Learning"> <meta name="dc.subject" content="SignificantDegree of Attributes"> <meta name="dc.subject" content="Scikitlearn K-Means Clustering"> <meta name="dc.subject" content="Weighted Data"> <meta name="dc.subject" content="UCI Machine Learning Data">  <meta name="prism.publicationName" content="Computer Science & Information Technology (CS & IT)"> <meta name="prism.publicationDate" content="28-11-2020"> <meta name="prism.volume" content="10"> <meta name="prism.number" content="16"> <meta name="prism.section" content="Article"> <meta name="prism.startingPage" content="93">   <meta name="citation_journal_title" content="Computer Science & Information Technology (CS & IT)"> <meta name="citation_publisher" content="AIRCC Publishing Corporation"> <meta name="citation_author" content="Byoung Jik Lee"> <meta name="citation_title" content="Extracting the Significant Degrees of Attributes in Unlabeled Data using Unsupervised Machine Learning"> <meta name="citation_online_date" content="28-11-2020"> <meta name="citation_volume" content="10"> <meta name="citation_issue" content="16"> <meta name="citation_firstpage" content="93"> <meta name="citation_author" content="Byoung Jik Lee"> <meta name="citation_doi" content="10.5121/csit.2020.101608"> <meta name="citation_abstract_html_url" content="https://aircconline.com/csit/abstract/v10n16/csit101608.html"> <meta name="citation_pdf_url" content="https://aircconline.com/csit/papers/vol10/csit101608.pdf">   <meta property="og:site_name" content="AIRCC" /> <meta property="og:type" content="article" /> <meta property="og:url" content="https://aircconline.com/csit/abstract/v10n16/csit101608.html"/> <meta property="og:title" content="Extracting the Significant Degrees of Attributes in Unlabeled Data using Unsupervised Machine Learning"> <meta property="og:description" content="We propose a valid approach to find the degree of important attributes in unlabeled dataset to improve the clustering performance. The significant degrees of attributes are extracted through the training of unsupervised simple competitive learning with the raw unlabeled data. These significant degrees are applied to the original dataset and generate the weighted dataset reflected by the degrees of influentialvalues for the set ofattributes. This work is simulated on the UCI Machine Learning repository dataset. The Scikit-learn K-Means clustering with raw data, scaled data, and the weighted data are tested. 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The significant degrees of attributes are extracted through the training of unsupervised simple competitive learning with the raw unlabeled data. These significant degrees are applied to the original dataset and generate the weighted dataset reflected by the degrees of influentialvalues for the set ofattributes. This work is simulated on the UCI Machine Learning repository dataset. The Scikit-learn K-Means clustering with raw data, scaled data, and the weighted data are tested. The result shows that the proposed approach improves the performance. </p> <h3>  Keywords</h3> <p class="#left right" style="text-align:justify">Unsupervised MachineLearning, Simple Competitive Learning, SignificantDegree of Attributes, Scikitlearn K-Means Clustering, Weighted Data, UCI Machine Learning Data.</p><br> <button type="button" id="button"><a target="blank" href="/csit/papers/vol10/csit101608.pdf">Full Text</a></button>   <button type="button" id="button"><a href="http://airccse.org/csit/V10N16.html">Volume 10, Number 16</a></button> <br><br><br><br><br> </div> <div id="right"> <div class="menu_right"> <ul> <li id="id"><a href="http://airccse.org/editorial.html">Editorial Board</a></li> <li><a href="http://airccse.org/arch.html">Archives</a></li> <li><a href="http://airccse.org/indexing.html">Indexing</a></li> <li><a href="http://airccse.org/faq.html" target="_blank">FAQ</a></li> </ul> </div> <div class="clear_left"></div> <br> </div> <div class="clear"></div> <div id="footer"> <table width="100%" > <tr> <td width="46%" class="F_menu"><a href="http://airccse.org/subscription.html">Subscription</a> <a href="http://airccse.org/membership.html">Membership</a> <a href="http://airccse.org/cscp.html">AIRCC CSCP</a> <a href="http://airccse.org/acontact.html">Contact Us</a> </td> <td width="54%" align="right"><a href="http://airccse.org/index.php"><img src="/csit/abstract/img/logo.gif" alt="" width="21" height="24" /></a><a href="http://www.facebook.com/AIRCCSE"><img src="/csit/abstract/img/facebook.jpeg" alt="" width="21" height="24" /></a><a href="https://twitter.com/AIRCCFP"><img src="/csit/abstract/img/twitter.jpeg" alt="" width="21" height="24" /></a><a href="http://cfptech.wordpress.com/"><img src="/csit/abstract/img/index1.jpeg" alt="" width="21" height="24" /></a></td> </tr> <tr><td height="25" colspan="2"> <p align="center">All Rights Reserved ® AIRCC</p> </td></tr> </table> </div> </div> </div> </div> </div> </div> </body> </html>

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Extracting the Significant Degrees of Attributes in Unlabeled Data using Unsupervised Machine Learning