{"created":"2020-09-01T14:11:32.528474+00:00","id":4133,"links":{},"metadata":{"_buckets":{"deposit":"db7cc209-12c6-4044-8d34-d3c3a00f068f"},"_deposit":{"id":"4133","owners":[],"pid":{"revision_id":0,"type":"recid","value":"4133"},"status":"published"},"_oai":{"id":"oai:meral.edu.mm:recid/4133","sets":["1582963302567:1597824273898"]},"communities":["ucsy"],"item_1583103067471":{"attribute_name":"Title","attribute_value_mlt":[{"subitem_1551255647225":"Comparison of Clustering with Self Organizing Map and Fuzzy C-Means Algorithm","subitem_1551255648112":"en"}]},"item_1583103085720":{"attribute_name":"Description","attribute_value_mlt":[{"interim":"Clustering partitions a set of objects into non-overlapping subsets called clusters such that objects inside each cluster are similar to each other and objects from different clusters are not similar. The set of non-overlapping clusters is called a partition. Neural networks are believed to possess some particularly valuable properties, since they are patterned after associative neural properties of the brain. Neural networks proceed by a process called learning. The Self-Organizing Map (SOM) is a stable neural network model for high-dimensional data analysis. Most classical clustering algorithms assign each data to exactly one cluster, thus forming a crisp partition of the given data, but fuzzy clustering allows for degrees of membership, to which data belongs to different clusters. The best known fuzzy clustering algorithm is fuzzy c-means (FCM) clustering algorithm which is straightforward generalization of classical crisp c-means algorithm. This system is implemented clustering multidimensional data by using SOM and FCM algorithms."}]},"item_1583103108160":{"attribute_name":"Keywords","attribute_value":[]},"item_1583103120197":{"attribute_name":"Files","attribute_type":"file","attribute_value_mlt":[{"accessrole":"open_access","date":[{"dateType":"Available","dateValue":"2019-08-06"}],"displaytype":"preview","filename":"55265.pdf","filesize":[{"value":"499 Kb"}],"format":"application/pdf","licensetype":"license_note","mimetype":"application/pdf","url":{"url":"https://meral.edu.mm/record/4133/files/55265.pdf"},"version_id":"71c8f24e-a568-45cf-989e-849911cd2274"}]},"item_1583103131163":{"attribute_name":"Journal articles","attribute_value_mlt":[{"subitem_issue":"","subitem_journal_title":"Fourth Local Conference on Parallel and Soft Computing","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":"Maung, Hsu Mon"},{"subitem_authors_fullname":"Win, Tha Pyay"}]}]},"item_1583108359239":{"attribute_name":"Upload type","attribute_value_mlt":[{"interim":"Publication"}]},"item_1583108428133":{"attribute_name":"Publication type","attribute_value_mlt":[{"interim":"Article"}]},"item_1583159729339":{"attribute_name":"Publication date","attribute_value":"2009-12-30"},"item_1583159847033":{"attribute_name":"Identifier","attribute_value":"http://onlineresource.ucsy.edu.mm/handle/123456789/1833"},"item_title":"Comparison of Clustering with Self Organizing Map and Fuzzy C-Means Algorithm","item_type_id":"21","owner":"1","path":["1597824273898"],"publish_date":"2019-08-06","publish_status":"0","recid":"4133","relation_version_is_last":true,"title":["Comparison of Clustering with Self Organizing Map and Fuzzy C-Means Algorithm"],"weko_creator_id":"1","weko_shared_id":-1},"updated":"2021-12-13T03:26:31.583231+00:00"}