Computationally Efficient Mining for Fuzzy Implication-Based Association Rules in Quantitative Databases

Abstract

Association rule mining is one of the focal points in research on knowledge discovery. While conventional approaches usually deal with databases with binary values, this paper presents an approach to discovering association rules (such as X \Rightarrow Y ) from quantitative datasets, which are commonly seen in real-world applications. Primary attention is paid to association rules with degrees of support and implication (ARsi), taking a more logic-oriented viewpoint for X-to-Y relationships. Fuzzy logic is applied to “discretization” of quantitative domains as well as to logic implication operations so as to remedy possible boundary problems due to sharp partitioning and facilitate fuzzy implication, respectively. In doing so, a mining algorithm (FIAR) is proposed to discover ARsi, in that several properties of t-norms and fuzzy implication operators (FIOs) are investigated to reduce times of scanning databases. Furthermore, simple rules are discussed and incorporated as optimization strategies into the algorithm. Finally, experiments with synthetic data as well as with real datasets are carried out to show the performance of the proposed algorithm.

Keywords:

• Data mining
• association rules
• fuzzy implication
• simple rules

Acknowledgements

The work was partly supported by the National Natural Science Foundation of China (79925001/70231010), and the Bilateral Scientific and Technological Cooperation Between China and Flanders/Czech.

Notes

Guoqing Chen received his Ph.D. degree from the Catholic University of Leuven, Belgium, in 1992. Currently he is Professor of Information Systems and Management at the School of Economics and Management, Tsinghua University, Beijing, China. Professor Chen has over 60 publications worldwide in many journals, books, and conference proceedings, including two books (on fuzzy data modeling and soft computing) published by Kluwer Academic Publishers, Boston, in 1998 and 1999, respectively. His areas of interest include data mining, databases, fuzzy logic, and IT management.

Peng Yan is a Ph.D. candidate at the Department of Management Sciences and Engineering, School of Economics and Management, Tsinghua University, Beijing, China. He has had several publications and is a researcher in a number of national and international research projects. His research interests include data mining and knowledge discovery in databases, fuzzy logic and data models.

Etienne E Kerre obtained his Ph.D. degree from Gent University, Belgium, in 1970. He has published more than 160 papers on fundamental as well as practical issues of fuzzy set theory in international journals and conference proceedings, including a book (Introduction to the Basic Principles of Fuzzy Set Theory and some of its Applications) published in 1991. He has been chair and member of many international conferences and journals. His areas of interest include fuzzy mathematics, information retrieval and databases, knowledge systems and discovery.