{"_buckets": {"deposit": "d2756807-1a73-4581-a586-f4adbbf1b891"}, "_deposit": {"created_by": 73, "id": "7930", "owner": "73", "owners": [73], "owners_ext": {"displayname": "", "username": ""}, "pid": {"revision_id": 0, "type": "depid", "value": "7930"}, "status": "published"}, "_oai": {"id": "oai:meral.edu.mm:recid/00007930", "sets": ["user-miit"]}, "communities": ["miit"], "item_1583103067471": {"attribute_name": "Title", "attribute_value_mlt": [{"subitem_1551255647225": "Mining Multilevel Single Dimension Association Rules Based On  Apriori Algorithm", "subitem_1551255648112": "en"}]}, "item_1583103085720": {"attribute_name": "Description", "attribute_value_mlt": [{"interim": "Data Mining, emerged during the late 1980s, is a broad term used to describe various methods for discovering patterns or knowledge hidden in large set of data. Many kinds of pattern can be discovered. This knowledge can be presented in multiple forms, such as rules tables, pie or bar charts, decision trees, or other visual representation. A kind of pattern often considered is association rules. Earlier work on association rule did not consider for association rule mining with the presence of concept hierarchy and restricted the items in association rules to the leaf-level items in the taxonomy (concept hierarchy).In fact, finding rules across different level of taxonomy is valuable. Mining association rules at multiple concept levels may lead to the discovery of more specific and concrete knowledge from the data. In this paper, a top-down progressive deepening method is developed for mining multiple level association rules from transaction database using the basic association mining algorithm, the Apriori."}]}, "item_1583103120197": {"attribute_name": "Files", "attribute_type": "file", "attribute_value_mlt": [{"accessrole": "open_access", "date": [{"dateType": "Available", "dateValue": "2021-01-29"}], "displaytype": "preview", "download_preview_message": "", "file_order": 0, "filename": "Mining Multilevel Single Dimension Association Rules Based On  Apriori Algorith.doc", "filesize": [{"value": "336 KB"}], "format": "application/msword", "future_date_message": "", "is_thumbnail": false, "licensetype": "license_0", "mimetype": "application/msword", "size": 336000.0, "url": {"url": "https://meral.edu.mm/record/7930/files/Mining Multilevel Single Dimension Association Rules Based On  Apriori Algorith.doc"}, "version_id": "7b59ca26-268b-483a-ad4b-ee9d41fa841e"}]}, "item_1583103147082": {"attribute_name": "Conference papers", "attribute_value_mlt": [{"subitem_acronym": "PSC", "subitem_c_date": "11.Nov. 2008", "subitem_conference_title": "3rd Conference on Parallel and Soft Computing (PSC\u0027 08)", "subitem_place": "UCSY, Myanmar", "subitem_website": "www.ucsy.edu.mm"}]}, "item_1583105942107": {"attribute_name": "Authors", "attribute_value_mlt": [{"subitem_authors": [{"subitem_authors_fullname": "Ei Khaing Win"}, {"subitem_authors_fullname": "Khaing Moe San"}, {"subitem_authors_fullname": "Thin Thin Htwe"}]}]}, "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": "2008-11-05"}, "item_title": "Mining Multilevel Single Dimension Association Rules Based On  Apriori Algorithm", "item_type_id": "21", "owner": "73", "path": ["1582963674932", "1597396989070"], "permalink_uri": "http://hdl.handle.net/20.500.12678/0000007930", "pubdate": {"attribute_name": "Deposited date", "attribute_value": "2008-11-05"}, "publish_date": "2008-11-05", "publish_status": "0", "recid": "7930", "relation": {}, "relation_version_is_last": true, "title": ["Mining Multilevel Single Dimension Association Rules Based On  Apriori Algorithm"], "weko_shared_id": -1}