{"created":"2020-08-30T13:56:28.136341+00:00","id":3117,"links":{},"metadata":{"_buckets":{"deposit":"8e653531-25ce-4ec3-944f-28a3de73ffd3"},"_deposit":{"id":"3117","owners":[],"pid":{"revision_id":0,"type":"recid","value":"3117"},"status":"published"},"_oai":{"id":"oai:meral.edu.mm:recid/3117","sets":["1582963413512:1596119372420"]},"communities":["ytu"],"item_1583103067471":{"attribute_name":"Title","attribute_value_mlt":[{"subitem_1551255647225":"Systematic Selection of Initial Centroid for K-Means Document Clustering System","subitem_1551255648112":"en"}]},"item_1583103085720":{"attribute_name":"Description","attribute_value_mlt":[{"interim":"

As the number of electronic documents generated
\nfrom  worldwide  source  increases,  it  is  hard  to  manually
\norganize,  analyze  and  present  these  documents  efficiently.
\nDocument  clustering  is  one  of  the  traditionally  data  mining
\ntechniques and an unsupervised learning paradigm. Fast and
\nhigh  quality  document  clustering  algorithms  play  an
\nimportant  role  in  helping  users  to  effectively  navigate,
\nsummarize and organize the information. K-Means algorithm
\nis  the  most  commonly  used  partitioned  clustering  algorithm
\nbecause it can be easily implemented and is the most efficient
\none in terms of execution times. However, the major problem
\nwith  this  algorithm  is  that  it  is  sensitive  to  the  selection  of
\ninitial  centroid  and  may  converge  to  local  optima.  The
\nalgorithm takes the initial cluster centre arbitrarily so it does
\nnot always guarantee good clustering results. Different initial
\ncluster  centres  often  lead  to  different  clustering  and  thus
\nprovide unstable clustering results. To overcome this problem,   
\nSystematic Selection of Initial Centroid for K-Means (SSIC K-
\nMeans)  approach  is  proposed  to  improve  the  quality  of
\nclustering  in  this  paper.  Unlike  the  traditional  K-Means
\nclustering, the proposed SSIC K-Means method can generate
\nthe  most  compact  and  stable  clustering  results  based  on
\nmaximum distance initial centroids points instead of random
\ninitial centroid points. In this paper, experimental results are
\npresented  in  F-measures  using  20  Newsgroup  standard
\ndatasets.  The  evaluations  demonstrate  that  the  proposed
\nsolution outperforms the other initialization methods and can
\nbe applied for other various standard datasets.

"}]},"item_1583103108160":{"attribute_name":"Keywords","attribute_value_mlt":[{"interim":"Document clustering"},{"interim":"Data mining"},{"interim":"K-Means"},{"interim":"Initial centroid"},{"interim":"SSIC K-Means"}]},"item_1583103120197":{"attribute_name":"Files","attribute_type":"file","attribute_value_mlt":[{"accessrole":"open_access","date":[{"dateType":"Available","dateValue":"2019-07-04"}],"displaytype":"preview","filename":"Systematic Selection of Initial Centroid for K-Means Document Clustering System.pdf","filesize":[{"value":"251 Kb"}],"format":"application/pdf","mimetype":"application/pdf","url":{"url":"https://meral.edu.mm/record/3117/files/Systematic Selection of Initial Centroid for K-Means Document Clustering System.pdf"},"version_id":"aa0b4c3e-0c8d-4e66-925c-d5edd590e0ad"}]},"item_1583103131163":{"attribute_name":"Journal articles","attribute_value_mlt":[{"subitem_issue":"","subitem_journal_title":"","subitem_pages":"","subitem_volume":""}]},"item_1583103147082":{"attribute_name":"Conference papers","attribute_value_mlt":[{"subitem_acronym":"","subitem_c_date":"","subitem_conference_title":"","subitem_part":"","subitem_place":"","subitem_session":"","subitem_website":""}]},"item_1583103211336":{"attribute_name":"Books/reports/chapters","attribute_value_mlt":[{"subitem_book_title":"","subitem_isbn":"","subitem_pages":"","subitem_place":"","subitem_publisher":""}]},"item_1583103233624":{"attribute_name":"Thesis/dissertations","attribute_value_mlt":[{"subitem_awarding_university":"","subitem_supervisor(s)":[{"subitem_supervisor":""}]}]},"item_1583105942107":{"attribute_name":"Authors","attribute_value_mlt":[{"subitem_authors":[{"subitem_authors_fullname":"Tin Thu Zar Win"},{"subitem_authors_fullname":"Moe Moe Aye"}]}]},"item_1583108359239":{"attribute_name":"Upload type","attribute_value_mlt":[{"interim":"Publication"}]},"item_1583108428133":{"attribute_name":"Publication type","attribute_value_mlt":[{"interim":"Conference paper"}]},"item_1583159729339":{"attribute_name":"Publication date","attribute_value":"2016-12-29"},"item_1583159847033":{"attribute_name":"Identifier","attribute_value":"10.5281/zenodo.3268434"},"item_title":"Systematic Selection of Initial Centroid for K-Means Document Clustering System","item_type_id":"21","owner":"1","path":["1596119372420"],"publish_date":"2019-07-04","publish_status":"0","recid":"3117","relation_version_is_last":true,"title":["Systematic Selection of Initial Centroid for K-Means Document Clustering System"],"weko_creator_id":"1","weko_shared_id":-1},"updated":"2021-12-13T05:47:14.691127+00:00"}