Genetic fuzzy rule selection is an effective approach to the design of accurate and interpretable fuzzy rule-based classifiers. It tries to minimize the complexity of fuzzy rule-based classifiers while maximizing their accuracy by selecting only a small number of fuzzy rules from a large number of candidate rules. One important issue in genetic fuzzy rule selection is the prescreening of candid...

recently, tuning the weights of the rules in fuzzy rule-base classification systems is researched in order to improve the accuracy of classification. in this paper, a margin-based optimization model, inspired by support vector machine classifiers, is proposed to compute these fuzzy rule weights. this approach not only  considers both accuracy and generalization criteria in a single objective fu...

Fuzzy data mining is used to extract fuzzy knowledge from linguistic or quantitative data. It is an extension of traditional data mining and the derived knowledge is relatively meaningful to human beings. In the past, we proposed a mining algorithm to find suitable membership functions for fuzzy association rules based on ant colony systems. In that approach, precision was limited by the use of...

The use of hierarchical taxonomies to organise information (or sets of objects) is a common approach for the semantic web and elsewhere, and is based on progressively finer granulations of objects. In many cases, seemingly crisp granulation disguises the fact that categories are based on loosely defined concepts which are better modelled by allowing graded membership. A related problem arises w...

Many approaches have been proposed for mining fuzzy association rules. The membership functions, which critically influence the final mining results, are difficult to define. In general, multiple criteria are considered when defining membership functions. In this paper, a multi-objective genetic-fuzzy mining algorithm is proposed for extracting membership functions and association rules from qu...

This paper proposes fuzzy modeling using obtained data. Fuzzy system is known as knowledge-based or rule-bases system. The most important part of fuzzy system is rule-base. One of problems of generation of fuzzy rule with training data is inconsistence data. Existence of inconsistence and uncertain states in training data causes high error in modeling. Here, Probability fuzzy system presents to...

In this paper we extend the problem of mining weighted association rules. A classical model of boolean and fuzzy quantitative association rule mining is adopted to address the issue of invalidation of downward closure property (DCP) in weighted association rule mining where each item is assigned a weight according to its significance w.r.t some user defined criteria. Most works on DCP so far st...

Extracting knowledge from large amount of data while preserving the sensitive information is an important issue in data mining. Providing security to sensitive data against unauthorized access has been a long term goal for the database security research community. Almost all the research in privacy preservation is limited to binary dataset. Business and scientific data contain both quantitative...

Data mining is concerned with developing algorithms and computational tools and techniques to help people extract patterns from data. In this paper an efficient data mining approach, which is based on fuzzy set theory and clonal selection algorithm, is proposed. The main motivation is to benefit from the global search performed by this kind of algorithms. Experimental results show the number of...