{"_buckets": {"deposit": "57470edf-09f1-449a-b6b2-6a965db1e42a"}, "_deposit": {"id": "3322", "owners": [], "pid": {"revision_id": 0, "type": "recid", "value": "3322"}, "status": "published"}, "_oai": {"id": "oai:meral.edu.mm:recid/3322", "sets": ["user-ucsy"]}, "communities": ["ucsy"], "item_1583103067471": {"attribute_name": "Title", "attribute_value_mlt": [{"subitem_1551255647225": "Frequent Pattern Extraction from Web Usage data using Hash based Association Rule", "subitem_1551255648112": "en"}]}, "item_1583103085720": {"attribute_name": "Description", "attribute_value_mlt": [{"interim": "World Wide Web is a huge repository ofweb pages and links. Users’ accesses are recordedin web logs. Because of the tremendous usage ofweb, the web log files are growing at a faster rateand the size is becoming huge. Web data mining isthe application of data mining techniques in webdata. Web Usage Mining applies miningtechniques in log data to extract the behavior ofusers which is used in various applications likepersonalized services, adaptive web sites,customer profiling, prefetching, creating attractiveweb sites etc., This paper presents web usagemining where frequent patterns of accessedpatterns are generated by using hashingapproach. The main problem in generatingassociation rules by Apriori algorithm isprocessing time. In this system, Apriori algorithmis improved by Hashing approach. Hashing is veryefficient for the generation of candidate largeitemsets. In addition, hashing employs effectivepruning techniques to reduce the transactiondatabase size. Generation of smaller candidatesets by hashing enables to effectively trim thetransaction database at a much earlier stage ofthe iterations, thereby reducing the computationalcost for later iterations significantly."}]}, "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", "download_preview_message": "", "file_order": 0, "filename": "235_PDFsam_PSC_final proof.pdf", "filesize": [{"value": "244 Kb"}], "format": "application/pdf", "future_date_message": "", "is_thumbnail": false, "licensetype": "license_free", "mimetype": "application/pdf", "size": 244000.0, "url": {"url": "https://meral.edu.mm/record/3322/files/235_PDFsam_PSC_final proof.pdf"}, "version_id": "9cc5f912-8858-4a21-8bea-90a211ba2f00"}]}, "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": "Swe, Thet Htoo"}, {"subitem_authors_fullname": "Aye, Nilar"}]}]}, "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/123456789/1084"}, "item_title": "Frequent Pattern Extraction from Web Usage data using Hash based Association Rule", "item_type_id": "21", "owner": "1", "path": ["1597824273898"], "permalink_uri": "http://hdl.handle.net/20.500.12678/0000003322", "pubdate": {"attribute_name": "Deposited date", "attribute_value": "2019-07-19"}, "publish_date": "2019-07-19", "publish_status": "0", "recid": "3322", "relation": {}, "relation_version_is_last": true, "title": ["Frequent Pattern Extraction from Web Usage data using Hash based Association Rule"], "weko_shared_id": -1}