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| Title | Multidimensional multigranularity data mining for health-care service management |
| Creator | 姜國輝 Chiang, J.K.;Chu, Chu C.-C. |
| Contributor | 資訊管理學系 |
| Key Words | 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 |
| Date | 2015-08 |
| Date Issued | 14-Aug-2017 15:35:47 (UTC+8) |
| Summary | 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. |
| Relation | 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 |
| Type | 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) | 2015-08 | en_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, (), 469-474 | en_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 118459 | zh_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 mining | en_US |
| dc.title (題名) | Multidimensional multigranularity data mining for health-care service management | en_US |
| dc.type (資料類型) | conference |