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題名 Multidimensional multigranularity data mining for health-care service management
作者 姜國輝
Chiang, J.K.;Chu, Chu C.-C.
貢獻者 資訊管理學系
關鍵詞 Algorithms; Data warehouses; Health care; Information management; Management science; Public relations; Association patterns; Customer relationship management; Different granularities; Health services; Healthcare services; Mining algorithms; Multi-granularity; Data mining
日期 2015-08
上傳時間 14-Aug-2017 15:35:47 (UTC+8)
摘要 Data Mining is getting increasingly important for discovering association patterns for health service innovation and Customer Relationship Management (CRM). Yet, there are deficits of existing data mining techniques. First of all, most of them perform a plain mining based on a predefined schemata through the data warehouse; however, a re-scan must be done whenever new attributes appear. Second, an association rule may be true on a certain granularity but fail on a smaller one and vice versa. Last but not least, they are usually designed to find either frequent or infrequent rules. In this article, we are going to invent more efficient and accurate approach with novel data structure and multidimensional mining algorithm to explore association patterns on different granularities and to find out portfolios of health-care service management. © 2015 Taylor & Francis Group, London.
關聯 Medicine Sciences and Bioengineering - Proceedings of the 2014 International Conference on Medicine Sciences and Bioengineering, ICMSB 2014, (), 469-474
Proceedings of the 2014 International Conference on Medicine Sciences and Bioengineering, ICMSB 2014; Kunming; China; 16 August 2014 到 17 August 2014; 代碼 118459
資料類型 conference
dc.contributor 資訊管理學系zh_Tw
dc.creator (作者) 姜國輝zh_TW
dc.creator (作者) Chiang, J.K.;Chu, Chu C.-C.en_US
dc.date (日期) 2015-08en_US
dc.date.accessioned 14-Aug-2017 15:35:47 (UTC+8)-
dc.date.available 14-Aug-2017 15:35:47 (UTC+8)-
dc.date.issued (上傳時間) 14-Aug-2017 15:35:47 (UTC+8)-
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/111939-
dc.description.abstract (摘要) Data Mining is getting increasingly important for discovering association patterns for health service innovation and Customer Relationship Management (CRM). Yet, there are deficits of existing data mining techniques. First of all, most of them perform a plain mining based on a predefined schemata through the data warehouse; however, a re-scan must be done whenever new attributes appear. Second, an association rule may be true on a certain granularity but fail on a smaller one and vice versa. Last but not least, they are usually designed to find either frequent or infrequent rules. In this article, we are going to invent more efficient and accurate approach with novel data structure and multidimensional mining algorithm to explore association patterns on different granularities and to find out portfolios of health-care service management. © 2015 Taylor & Francis Group, London.en_US
dc.format.extent 177 bytes-
dc.format.mimetype text/html-
dc.relation (關聯) Medicine Sciences and Bioengineering - Proceedings of the 2014 International Conference on Medicine Sciences and Bioengineering, ICMSB 2014, (), 469-474en_US
dc.relation (關聯) Proceedings of the 2014 International Conference on Medicine Sciences and Bioengineering, ICMSB 2014; Kunming; China; 16 August 2014 到 17 August 2014; 代碼 118459zh_TW
dc.subject (關鍵詞) Algorithms; Data warehouses; Health care; Information management; Management science; Public relations; Association patterns; Customer relationship management; Different granularities; Health services; Healthcare services; Mining algorithms; Multi-granularity; Data miningen_US
dc.title (題名) Multidimensional multigranularity data mining for health-care service managementen_US
dc.type (資料類型) conference