標題: A fuzzy data mining algorithm for finding sequential patterns
作者: Hu, YC
Chen, RS
Tzeng, GH
Shieh, JH
科技管理研究所
資訊管理與財務金融系 註:原資管所+財金所
Institute of Management of Technology
Department of Information Management and Finance
關鍵字: data mining;sequential patterns;fuzzy sets;knowledge acquisition
公開日期: 1-Apr-2003
摘要: Since fuzzy knowledge representation can facilitate interaction between an expert system and its users, the effective construction of a fuzzy knowledge base is important. Fuzzy sequential patterns described by natural language are one type of fuzzy knowledge representation, and can thus be helpful in building a prototype fuzzy knowledge base. We define that a fuzzy sequence is an ordered fist of frequent fuzzy grids, and the length of a fuzzy sequence is the number of frequent fuzzy grids in the frequent fuzzy sequence. Frequent fuzzy grids and frequent fuzzy sequences can be determined by comparing individual fuzzy supports with the user-specified minimum fuzzy support. A fuzzy sequential pattern is just a frequent fuzzy sequence, but it is not contained in any other frequent fuzzy sequence. In this paper, an effective algorithm called the Fuzzy Grids Based Sequential Patterns Mining Algorithm (FGBSPMA) is proposed to generate fuzzy sequential patterns. A numerical example is used to show an analysis of the user visit to websites, demonstrating the usefulness of the proposed algorithm.
URI: http://dx.doi.org/10.1142/S0218488503002004
http://hdl.handle.net/11536/28015
ISSN: 0218-4885
DOI: 10.1142/S0218488503002004
期刊: INTERNATIONAL JOURNAL OF UNCERTAINTY FUZZINESS AND KNOWLEDGE-BASED SYSTEMS
Volume: 11
Issue: 2
起始頁: 173
結束頁: 193
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