{"_buckets": {"deposit": "e018f6b8-6e1f-4803-9296-b5a155735b11"}, "_deposit": {"created_by": 45, "id": "5381", "owner": "45", "owners": [45], "owners_ext": {"displayname": "", "email": "dimennyaung@uit.edu.mm", "username": ""}, "pid": {"revision_id": 0, "type": "recid", "value": "5381"}, "status": "published"}, "_oai": {"id": "oai:meral.edu.mm:recid/5381"}, "item_1583103067471": {"attribute_name": "Title", "attribute_value_mlt": [{"subitem_1551255647225": "Mining the Text using Association Rule Mining Technique", "subitem_1551255648112": "en"}]}, "item_1583103085720": {"attribute_name": "Description", "attribute_value_mlt": [{"interim": "As the amount of text available in electronic form continues to increase at alarming rate, the tools to manage these textual resources effectively will become critical. Information Retrieval System tries to save the users access time by classifying the documents and clustering the documents because users spend a lot of time to find documents or information from texts. Therefore, text mining is the most popular and it is necessary to solve this problem. The largest amount of work in text mining has been in the areas of categorization, classification and clustering of documents. Text mining has many methods to find the useful information. Among these methods, association rule mining is very suitable for finding the most frequent words that occur in the document collection. Association rule analysis is the task of discovering association rules that occur frequently in a given text sets. Our proposed system had been developed by applying the preprocessing steps of text mining system and Apriori algorithm for finding the pairs of most frequent words. These frequent words are associated with each other and they can provide the trained texts for the document classification."}]}, "item_1583103108160": {"attribute_name": "Keywords", "attribute_value_mlt": [{"interim": "Data Mining"}, {"interim": "Text Mining"}]}, "item_1583103120197": {"attribute_name": "Files", "attribute_type": "file", "attribute_value_mlt": [{"accessrole": "open_access", "date": [{"dateType": "Available", "dateValue": "2020-09-16"}], "displaytype": "preview", "download_preview_message": "", "filename": "Mining the Text using Association Rule Mining Technique.pdf", "filesize": [{"value": "190 Kb"}], "format": "application/pdf", "future_date_message": "", "is_thumbnail": false, "licensetype": "license_0", "size": 190000, "url": {"url": "https://meral.edu.mm/record/5381/files/Mining the Text using Association Rule Mining Technique.pdf"}, "version_id": "5ac09091-1919-4a99-95d1-8fde649f825d"}]}, "item_1583103147082": {"attribute_name": "Conference papers", "attribute_value_mlt": [{"subitem_acronym": "AICT", "subitem_c_date": "18-19 September, 2008", "subitem_place": "Myanmar"}]}, "item_1583105942107": {"attribute_name": "Authors", "attribute_value_mlt": [{"subitem_authors": [{"subitem_authors_fullname": "Myint Myint Lwin"}, {"subitem_authors_fullname": "Myint Thuzar Tun"}]}]}, "item_1583108359239": {"attribute_name": "Upload type", "attribute_name_i18n": "Upload type", "attribute_value": "Publication"}, "item_1583108428133": {"attribute_name": "Publication type", "attribute_name_i18n": "Publication type", "attribute_value": "Conference paper"}, "item_1583159729339": {"attribute_name": "Publication date", "attribute_name_i18n": "Publication date", "attribute_value": "2008-09-19"}, "item_title": "Mining the Text using Association Rule Mining Technique", "item_type_id": "21", "owner": "45", "path": ["1582963342780/1596102391527"], "permalink_uri": "http://hdl.handle.net/20.500.12678/0000005381", "pubdate": {"attribute_name": "Deposited date", "attribute_value": "2020-09-16"}, "publish_date": "2020-09-16", "publish_status": "0", "recid": "5381", "relation": {}, "relation_version_is_last": true, "title": ["Mining the Text using Association Rule Mining Technique"], "weko_shared_id": -1}