{"created":"2020-08-30T13:56:20.888250+00:00","id":3116,"links":{},"metadata":{"_buckets":{"deposit":"033d64ba-46ea-44db-a6f9-1ecffc6ed024"},"_deposit":{"id":"3116","owners":[],"pid":{"revision_id":0,"type":"recid","value":"3116"},"status":"published"},"_oai":{"id":"oai:meral.edu.mm:recid/3116","sets":["1582963413512:1596119372420"]},"communities":["ytu"],"item_1583103067471":{"attribute_name":"Title","attribute_value_mlt":[{"subitem_1551255647225":"Modified K-Means for Document Clustering System","subitem_1551255648112":"en"}]},"item_1583103085720":{"attribute_name":"Description","attribute_value_mlt":[{"interim":"

In  today’s  era  of  World  Wide  Web,  there  is  a
\ntremendous  proliferation  in  the  amount  of  digitized  text
\ndocuments. As there is huge collection of documents on the web,
\nthere  is  a  need  of  grouping  the  set  of  documents  into  clusters.
\nDocument  clustering  plays  an  important  role  in  effectively
\nnavigating  and  organizing  the  documents.  K-Means  clustering
\nalgorithm  is  the  most  commonly  document  clustering  algorithm
\nbecause it can be easily implemented and is the most efficient one
\nin  terms  of  execution  times.  The  major  problem  with  this
\nalgorithm is that it is quite sensitive to selection of initial cluster
\ncentroids. The algorithm takes the initial cluster center arbitrarily
\nso it does not always promise good clustering results. If the initial
\ncentroids  are  incorrectly  determined,  the  remaining  data  points
\nwith the same similarity scores may fall into the different clusters
\ninstead of the same cluster. To overcome this problem,   modified
\nK-Means  approach  is  proposed  to  improve  the  quality  of
\nclustering  in  this  paper.    Unlike  the  traditional  K-Means
\nclustering, the proposed K-Means method can generate the most
\ncompact and stable clustering results based on maximum distance
\ninitial centroids points instead of random initial centroid points.
\nMoreover,  the  similar  data  points  are  clustered  based  on
\nmaximum probability distribution of data points.  Therefore, the
\nproposed method is more effective and converges to more accurate
\nclusters than original K-Means clustering method. In this paper,
\nexperimental  results  are  presented  in  F-measure  using  20-News
\nGroup standard dataset.

"}]},"item_1583103108160":{"attribute_name":"Keywords","attribute_value_mlt":[{"interim":"Document clustering"},{"interim":"F-measure"},{"interim":"Initial centroid"},{"interim":"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":"Modified K-Means for Document Clustering System-2016.pdf","filesize":[{"value":"311 Kb"}],"format":"application/pdf","mimetype":"application/pdf","url":{"url":"https://meral.edu.mm/record/3116/files/Modified K-Means for Document Clustering System-2016.pdf"},"version_id":"6026e71d-9240-4bb0-99cb-d5f012f59eab"}]},"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":"2016-10-01"},"item_1583159847033":{"attribute_name":"Identifier","attribute_value":"10.5281/zenodo.3268423"},"item_title":"Modified K-Means for Document Clustering System","item_type_id":"21","owner":"1","path":["1596119372420"],"publish_date":"2019-07-04","publish_status":"0","recid":"3116","relation_version_is_last":true,"title":["Modified K-Means for Document Clustering System"],"weko_creator_id":"1","weko_shared_id":-1},"updated":"2021-12-13T05:47:05.798017+00:00"}