Classification with degree of membership: A fuzzy approach

Wai Ho Au, Chun Chung Chan

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

44 Citations (Scopus)

Abstract

Classification is an important topic in data mining research. It is concerned with the prediction of the values of some attribute in a database based on other attributes. To tackle this problem, most of the existing data mining algorithms adopt either a decision tree based approach or an approach that requires users to provide some user-specified thresholds to guide the search for interesting rules. In this paper, we propose a new approach based on the use of an objective interestingness measure to distinguish interesting rules from uninteresting ones. Using linguistic terms to represent the revealed regularities and exceptions, this approach is especially useful when the discovered rules are presented to human experts for examination because of the afinity with the human knowledge representation. The use of fuzzy technique allows the prediction of attribute values to be associated with degree of membership. Our approach is, therefore, able to deal with the cases that an object can belong to more than one class. For example, a person can suffer from cold and fever to certain extent at the same time. Furthermore, our approach is more resilient to noise and missing data values because of the use of fuzzy technique. To evaluate the performance of our approach, we tested it using several real-life databases. The experimental results show that it can be very effective at data mining tasks. In fact, when compared to popular data mining algorithms, our approach can be better able to uncover useful rules hidden in databases.
Original languageEnglish
Title of host publicationProceedings - 2001 IEEE International Conference on Data Mining, ICDM'01
Pages35-42
Number of pages8
Publication statusPublished - 1 Dec 2001
Event1st IEEE International Conference on Data Mining, ICDM'01 - San Jose, CA, United States
Duration: 29 Nov 20012 Dec 2001

Conference

Conference1st IEEE International Conference on Data Mining, ICDM'01
CountryUnited States
CitySan Jose, CA
Period29/11/012/12/01

ASJC Scopus subject areas

  • Engineering(all)

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