A Fuzzy Mining Algorithm for Association-Rule Knowledge Discovery" (2005). ABSTRACT ABSTRASCT Due to increasing use of very large database and data warehouses, discovering useful knowledge from transactions is becoming an important research area. On the other hand, using fuzzy classification in data mining has been developed in recent years. Hong and Lee proposed a general learning method that ...

designing an effective criterion for selecting the best rule is a major problem in theprocess of implementing fuzzy learning classifier (flc) systems. conventionally confidenceand support or combined measures of these are used as criteria for fuzzy rule evaluation. in thispaper new entities namely precision and recall from the field of information retrieval (ir)systems is adapted as alternative...

The problem of mining association rules for fuzzy quantitative items was introduced and an algorithm proposed in [5]. However, the algorithm assumes that fuzzy sets are given. In this paper we propose a method to find the fuzzy sets for each quantitative attribute in a database by using clustering techniques. We present a scheme for finding the optimal partitioning of a data set during the clus...

Data mining is the process of extracting desirable knowledge or interesting patterns from existing databases for specific purposes. Many types of knowledge and technology have been proposed for data mining. Among them, finding association rules from transaction data is most commonly seen. Most studies have shown how binary valued transaction data may be handled. Transaction data in real-world a...

⎯This paper presents a new approach for solving the paradoxical Blackman's Association Problem. It utilizes the recently defined new class fusion rule based on fuzzy Tconorm/T-norm operators together with DezertSmarandache theory based, relative variations of generalized pignistic probabilities measure of correct associations, defined from a partial ordering function of hyper-power set. The abi...

This paper presents an evolutionary learning algorithm to facilitate the design of fuzzy controllers for mobile robots. It discusses the concepts, feasibility, bene ts and limitations of current evolutionary techniques for fuzzy rule discovery and tuning. We propose an evolution strategy that optimizes the gain factors in the conclusion part of TakagiSugeno-Kang type fuzzy rules. We describe tw...

The effective development of data mining techniques for the discovery of knowledge from training samples for classification problems in industrial engineering is necessary in applications, such as group technology. This paper proposes a learning algorithm, which can be viewed as a knowledge acquisition tool, to effectively discover fuzzy association rules for classification problems. The conseq...

due to the rapid growth of data mining technology, obtaining private data on users through this technology becomes easier. association rules mining is one of the data mining techniques to extract useful patterns in the form of association rules. one of the main problems in applying this technique on databases is the disclosure of sensitive data by endangering security and privacy. hiding the as...