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    题名: Multidimensional data mining of association patterns in various granularities for healthcare service portfolio management
    作者: Chiang, Johannes K.
    姜國輝
    贡献者: 資管系
    关键词: Algorithms;Data warehouses;Engineering research;Health care;Association pattern;Concept taxonomy;Healthcare services;Multidimensional data mining;Data mining
    日期: 2007
    上传时间: 2015-07-13 16:29:20 (UTC+8)
    摘要: Data Mining is one of the most significant tools for discovering association patterns that are useful for health services, Customer Relationship Management (CRM) etc. Yet, there are some drawbacks in existing mining techniques. Since most of them perform the flat mining based on pre-defined schemata through the data warehouse as a whole, a re-scan must be done whenever new attributes are added. Secondly, an association rule may be true on a certain granularity but fail on a smaller one and vise verse. And, they are used to find either frequent or infrequent rules. With regard to healthcare service management, this research aims at providing a novel data schema and an algorithm to solve the aforementioned problems. A forest of concept taxonomies is applied for representing healthcare knowledge space. On top of this structure, the mining process is formulated as a process of finding the largeitemsets, generating, updating and output the association patterns that represent portfolios of healthcare services. Crucial mechanisms in each step will be clarified in this paper. At last, this paper presents experimental results regarding efficiency, scalability, information loss, etc. of the proposed approach to prove its advantages. © 2007 IEEE.
    關聯: IEEM 2007: 2007 IEEE International Conference on Industrial Engineering and Engineering Management
    2007 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2007,2 December 2007 through 4 December 2007,
    数据类型: conference
    DOI 連結: http://dx.doi.org/10.1109/IEEM.2007.4419245
    DOI: 10.1109/IEEM.2007.4419245
    显示于类别:[資訊管理學系] 會議論文

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