General fuzzy association rules mining focuses on finding out the fuzzy itemsets or fuzzy attributes which frequently occur together. But two fuzzy itemsets which frequently occur together can not imply that there is always an interesting relationship between them. In this paper, we develop an alternative framework for mining interesting relationship between fuzzy itemsets based on fuzzy correl...

Several approaches generalizing crisp association rules to fuzzy association rules have been proposed. In an our previous paper we introduced a pair of confidence measures for crisp association rules from which one can be obtained the majority known quality measures. In this paper, starting from these results we give an extension to fuzzy association rules.

Data mining is sorting through data to identify patterns and establish relationships. Association rule mining is a well established method of data mining that identifies significant correlations between items in transactional data. Measures like support count, comprehensibility and interestingness, used for evaluating a rule can be thought of as different objectives of association rule mining p...

Association rule mainly focuses on large transactional databases. In association rule mining all items are considered with equal weightage. But it is not suitable for all datasets. The weight should be considered based on the importance of the item. In our previous work HITS algorithm (Hyperlink Induced Topic Search) is used to find the weight of an item w-support is calculated for generating f...

Data mining is the analysis step of the "Knowledge Discovery in Databases" process, or KDD. It is the process that results in the discovery of new patterns in large data sets. It utilizes methods at the intersection of artificial intelligence, machine learning, statistics, and database systems. The overall goal of the data mining process is to extract knowledge from an existing data set and tra...

Association rule mining is the most popular technique in the area of data mining. The main task of this technique is to find the frequent patterns by using minimum support thresholds decided by the user. The Apriori algorithm is a classical algorithm among association rule mining techniques. This algorithm is inefficient because it scans the database many times. Second, if the database is large...

From the definition of fuzzy sets by Zadeh in 1965, fuzzy logic has become a significant area of interest for researchers on artificial intelligence. In particular, Professor Mamdani was the pioneer who investigated the use of fuzzy logic for interpreting the human derived control rules, and therefore his work was considered a milestone application of this theory. In this work, we aim to carry ...

This work proposes a classification-rule discovery algorithm integrating artificial immune systems and fuzzy systems. The algorithm consists of two parts: a sequential covering procedure and a rule evolution procedure. Each antibody (candidate solution) corresponds to a classification rule. The classification of new examples (antigens) considers not only the fitness of a fuzzy rule based on the...