{"created":"2020-09-01T10:06:58.560404+00:00","id":3414,"links":{},"metadata":{"_buckets":{"deposit":"cabe6c60-60a6-4f0e-b0c7-9e17eb1a28c3"},"_deposit":{"id":"3414","owners":[],"pid":{"revision_id":0,"type":"recid","value":"3414"},"status":"published"},"_oai":{"id":"oai:meral.edu.mm:recid/3414","sets":["1582963302567:1597824273898"]},"communities":["ucsy"],"item_1583103067471":{"attribute_name":"Title","attribute_value_mlt":[{"subitem_1551255647225":"An Approach to CRM: Usage Behavior Analysis with K-Means Algorithm","subitem_1551255648112":"en"}]},"item_1583103085720":{"attribute_name":"Description","attribute_value_mlt":[{"interim":"Nowadays, information and communication technologies are developing with great speed. In the advent of information era, information technology has developed rapidly and has become significant for every business, particularly the credit card business. The purpose of this paper is to classify the selected customers into clusters using k-Means Algorithms and then decide with the concept of customer relationship management (CRM) to identify high profit, gold customers. The result of this study will benefit the organization in exploiting it to bring about competitive advantage and being able to retain customers as well as attract potential ones. The objective of this paper is to help the managers to segment the customers and identify which customers to target for credit card business services by using behavior analysis. Consequently, the results show that there is a clear distinction between the segments in terms of customer behavior."}]},"item_1583103108160":{"attribute_name":"Keywords","attribute_value_mlt":[{"interim":"Data Mining"},{"interim":"Customer Relationship Management"},{"interim":"k-means algorithm"},{"interim":"Credit Card"}]},"item_1583103120197":{"attribute_name":"Files","attribute_type":"file","attribute_value_mlt":[{"accessrole":"open_access","date":[{"dateType":"Available","dateValue":"2019-07-22"}],"displaytype":"preview","filename":"psc2010paper (223).pdf","filesize":[{"value":"287 Kb"}],"format":"application/pdf","licensetype":"license_note","mimetype":"application/pdf","url":{"url":"https://meral.edu.mm/record/3414/files/psc2010paper (223).pdf"},"version_id":"024714a6-ddd1-4326-a33c-69e2e9218d3c"}]},"item_1583103131163":{"attribute_name":"Journal articles","attribute_value_mlt":[{"subitem_issue":"","subitem_journal_title":"Fifth 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":"Kyaw, May Thu"},{"subitem_authors_fullname":"Lwin, Mar Mar"}]}]},"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":"2010-12-16"},"item_1583159847033":{"attribute_name":"Identifier","attribute_value":"http://onlineresource.ucsy.edu.mm/handle/123456789/1168"},"item_title":"An Approach to CRM: Usage Behavior Analysis with K-Means Algorithm","item_type_id":"21","owner":"1","path":["1597824273898"],"publish_date":"2019-07-22","publish_status":"0","recid":"3414","relation_version_is_last":true,"title":["An Approach to CRM: Usage Behavior Analysis with K-Means Algorithm"],"weko_creator_id":"1","weko_shared_id":-1},"updated":"2021-12-13T00:57:05.757840+00:00"}