{"_buckets": {"deposit": "5c03f0d6-f20f-4852-8f25-f1c59c3eda0e"}, "_deposit": {"id": "4311", "owners": [], "pid": {"revision_id": 0, "type": "recid", "value": "4311"}, "status": "published"}, "_oai": {"id": "oai:meral.edu.mm:recid/4311", "sets": ["user-ucsy"]}, "communities": ["ucsy"], "item_1583103067471": {"attribute_name": "Title", "attribute_value_mlt": [{"subitem_1551255647225": "Pattern Discovery using Association Rule Mining on Clustered Data", "subitem_1551255648112": "en_US"}]}, "item_1583103085720": {"attribute_name": "Description", "attribute_value_mlt": [{"interim": "Many organizations use World Wide Web for multipurposeplatform during these days. It is very important to understand how a website is being used by users. Web usage mining also known as web logmining, aims to discover interesting and frequent user access patternsfrom web browsing data that are stored in web server logs, proxy serverlogs or browser logs. Web usage mining involves the automatic discoveryof patterns from one or more Web servers using web log data. UsageMining tools discover and predict user behavior, in order to helpdesigner, improve the web site, attract visitors, or give regular users apersonalized and adaptive service. In this thesis, the aim is to findfrequent user access pattern from web log entries. Combined effort ofclustering and association rule mining is used to apply for patterndiscovery. The 30 web log files are used from United Nations HighCommissioner for Refugees. Density-based clustering spatial clusteringapplication with noise (DBSCAN) has been used to group the users basedon their access patterns and Apriori algorithm is applied to generatefrequent user access patterns. As DBSCAN groups the user based on theiraccess patterns, those users who don’t share the similar access patternsare removed. Hence clustering reduces the data size and Apriori generatesconcise and relevant rules. The result from this system is highly dependson the parameters provided by users. This system is implemented usingpython programming language and SQLite is used a storage layer."}]}, "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": "2019-09-23"}], "displaytype": "preview", "download_preview_message": "", "file_order": 0, "filename": "HtunZawOo.pdf", "filesize": [{"value": "1941 Kb"}], "format": "application/pdf", "future_date_message": "", "is_thumbnail": false, "licensetype": "license_free", "mimetype": "application/pdf", "size": 1941000.0, "url": {"url": "https://meral.edu.mm/record/4311/files/HtunZawOo.pdf"}, "version_id": "e71f6329-0a4f-4980-aba9-ed2c5a66bb1d"}]}, "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": "University of Computer Studies, Yangon", "subitem_supervisor(s)": [{"subitem_supervisor": ""}]}]}, "item_1583105942107": {"attribute_name": "Authors", "attribute_value_mlt": [{"subitem_authors": [{"subitem_authors_fullname": "Oo, Htun Zaw"}]}]}, "item_1583108359239": {"attribute_name": "Upload type", "attribute_value_mlt": [{"interim": "Publication"}]}, "item_1583108428133": {"attribute_name": "Publication type", "attribute_value_mlt": [{"interim": "Thesis"}]}, "item_1583159729339": {"attribute_name": "Publication date", "attribute_value": "2018-08"}, "item_1583159847033": {"attribute_name": "Identifier", "attribute_value": "http://onlineresource.ucsy.edu.mm/handle/123456789/2250"}, "item_title": "Pattern Discovery using Association Rule Mining on Clustered Data", "item_type_id": "21", "owner": "1", "path": ["1597824322519"], "permalink_uri": "http://hdl.handle.net/20.500.12678/0000004311", "pubdate": {"attribute_name": "Deposited date", "attribute_value": "2019-09-23"}, "publish_date": "2019-09-23", "publish_status": "0", "recid": "4311", "relation": {}, "relation_version_is_last": true, "title": ["Pattern Discovery using Association Rule Mining on Clustered Data"], "weko_shared_id": -1}