標題: Finding fuzzy classification rules using data mining techniques
作者: Hu, YC
Chen, RS
Tzeng, GH
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;fuzzy sets;classification problems;genetic algorithms
公開日期: 1-Jan-2003
摘要: Data mining techniques can be used to discover useful patterns by exploring and analyzing data, so, it is feasible to incorporate data mining techniques into the classification process to discover useful patterns or classification rules from training samples. This paper thus proposes a data mining technique to discover fuzzy classification rules based on the well-known Apriori algorithm. Significantly, since it is difficult for users to specify the minimum fuzzy support used to determine the frequent fuzzy grids or the minimum fuzzy confidence used to determine the effective classification rules derived from frequent fuzzy grids, therefore the genetic algorithms are incorporated into the proposed method to determine those two thresholds with binary chromosomes. For classification generalization ability, the simulation results from the iris data and the appendicitis data demonstrate that the proposed method performs well in comparison with other classification methods. (C) 2002 Elsevier Science B.V. All rights reserved.
URI: http://dx.doi.org/10.1016/S0167-8655(02)00273-8
http://hdl.handle.net/11536/28232
ISSN: 0167-8655
DOI: 10.1016/S0167-8655(02)00273-8
期刊: PATTERN RECOGNITION LETTERS
Volume: 24
Issue: 1-3
起始頁: 509
結束頁: 519
Appears in Collections:Articles


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