{"created":"2020-09-01T09:57:50.467419+00:00","id":3333,"links":{},"metadata":{"_buckets":{"deposit":"32d4f69e-5ab8-4a7a-b481-b4f89c42d3b2"},"_deposit":{"id":"3333","owners":[],"pid":{"revision_id":0,"type":"recid","value":"3333"},"status":"published"},"_oai":{"id":"oai:meral.edu.mm:recid/3333","sets":["1582963302567:1597824273898"]},"communities":["ucsy"],"item_1583103067471":{"attribute_name":"Title","attribute_value_mlt":[{"subitem_1551255647225":"Web Usage Mining Using Clustering and Association Rule Mining","subitem_1551255648112":"en"}]},"item_1583103085720":{"attribute_name":"Description","attribute_value_mlt":[{"interim":"Data mining methods are used to discoverthe behaviour of the users. Therefore, the dataused for the mining purpose must be qualified forthe data cleaning stage and must be consideredand planned efficiently to meet the requirement.For this reason, the data cleaning of the preprocessingstage becomes the essential key.Similarity measurement method is used to discoverweb usage data that have same category or usagepurpose for clustering. Association rule mininguses the clustered data to generate rules thatdiscover the patterns of interest.This proposed system presents web usage miningusing data mining methods. The main componentsthat are included in this system are thepreprocessing of web access log, computingsimilarity measurement using Jaccard coefficient,clustering the web pages using K-Mean Algorithmand finally the generation of rules for frequentpattern of web pages using Apriori Algorithm forinteresting relationships among web pages ingiven web usage data set."}]},"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":"2019-07-19"}],"displaytype":"preview","filename":"245_PDFsam_PSC_final proof.pdf","filesize":[{"value":"137 Kb"}],"format":"application/pdf","licensetype":"license_note","mimetype":"application/pdf","url":{"url":"https://meral.edu.mm/record/3333/files/245_PDFsam_PSC_final proof.pdf"},"version_id":"98b926b4-ee63-4ba8-9320-98a65ec0a475"}]},"item_1583103131163":{"attribute_name":"Journal articles","attribute_value_mlt":[{"subitem_issue":"","subitem_journal_title":"Eighth 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":"Thwin, Aye Theingi"},{"subitem_authors_fullname":"Kham, Nang Saing Moon"}]}]},"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":"2017-12-27"},"item_1583159847033":{"attribute_name":"Identifier","attribute_value":"http://onlineresource.ucsy.edu.mm/handle/123456789/1094"},"item_title":"Web Usage Mining Using Clustering and Association Rule Mining","item_type_id":"21","owner":"1","path":["1597824273898"],"publish_date":"2019-07-19","publish_status":"0","recid":"3333","relation_version_is_last":true,"title":["Web Usage Mining Using Clustering and Association Rule Mining"],"weko_creator_id":"1","weko_shared_id":-1},"updated":"2021-12-13T06:02:12.103834+00:00"}