Fuzzy associative matrix

About: Fuzzy associative matrix is a research topic. Over the lifetime, 8027 publications have been published within this topic receiving 194790 citations.

Some aspects of intuitionistic fuzzy sets

Abstract: We first discuss the significant role that duality plays in many aggregation operations involving intuitionistic fuzzy subsets We then consider the extension to intuitionistic fuzzy subsets of a number of ideas from standard fuzzy subsets In particular we look at the measure of specificity We also look at the problem of alternative selection when decision criteria satisfaction is expressed using intuitionistic fuzzy subsets We introduce a decision paradigm called the method of least commitment We briefly look at the problem of defuzzification of intuitionistic fuzzy subsets

A GA-based Fuzzy Mining Approach to Achieve a Trade-off Between Number of Rules and Suitability of Membership Functions

Abstract: Data mining is most commonly used in attempts to induce association rules from transaction data. Transactions in real-world applications, however, usually consist of quantitative values. This paper thus proposes a fuzzy data-mining algorithm for extracting both association rules and membership functions from quantitative transactions. We present a GA-based framework for finding membership functions suitable for mining problems and then use the final best set of membership functions to mine fuzzy association rules. The fitness of each chromosome is evaluated by the number of large 1-itemsets generated from part of the previously proposed fuzzy mining algorithm and by the suitability of the membership functions. Experimental results also show the effectiveness of the framework.

An effective algorithm for discovering fuzzy rules in relational databases

Abstract: We present a novel technique, called F-APACS, for discovering fuzzy association rules in relational databases. F-APACS employs linguistic terms to represent the revealed regularities and exceptions. The definitions of these linguistic terms are based on fuzzy set theory and the association rules expressed in them are called fuzzy association rules. To discover these rules, F-APACS utilizes an objective interestingness measure when determining if two attribute values are related. This measure is defined in terms of an "adjusted difference" between observed and expected frequency counts. The use of such a measure has the advantage that no user-supplied thresholds are required. In addition to this interestingness measure, F-APACS has another unique feature that it provides a mechanism to allow quantitative values be inferred from the rules. Such feature, as shown here, make F-APACS very effective at various data mining tasks.