On Genetic-Fuzzy Data Mining Techniques

Summary form only given. Data mining is commonly used in attempts to induce association rules from transaction data. Most previous studies focused on mining from binary valued data. Transactions in real-world applications, however, usually consist of quantitative values. Designing a sophisticated data-mining algorithm able to deal with various types of data presents a challenge to workers in this research field. In this article, the author first introduces some techniques for mining fuzzy association rules from quantitative transactions when the membership functions are known. He proposes several GA-based fuzzy data-mining methods for automatically extracting membership functions for the rules. All the genetic-fuzzy mining methods first use evolutional computation to find membership functions suitable for mining problems and then use the final best set of membership functions to mine fuzzy association rules. Experimental results show that the designed fitness functions can avoid the formation of bad kinds of membership functions and can provide important mining results to users.