The association rules, discovered by traditional support–confidence based algorithms, provide us with concise statements of potentially useful information hidden in databases. However, only considering the constraints of minimum support and minimum confidence is far from satisfying in many cases. In this paper, we propose a fuzzy method to formulate how interesting an association rule may be. I...

Classification based on association rules is considered to be effective and advantageous in many cases. However, there is a so-called "sharp boundary" problem in association rules mining with quantitative attribute domains. This paper aims at proposing an associative classification approach, namely Classification with Fuzzy Association Rules (CFAR), where fuzzy logic is used in partitioning the...

Data mining methods including association rule mining and frequent episode mining have been applied to the intrusion detection problem. In other work, we have introduced modifications of these methods that mine fuzzy association rules and fuzzy frequent episodes and have described off-line methods that utilize these fuzzy methods for anomaly detection from audit data. In this paper we describe ...

One of the core tasks of Knowledge Discovery in Databases (KDD) is the mining of association rules. In this paper, truth values of association rules are discussed. Firstly, two knowledge bases of association rules are fixed, i.e., information system A and a fixed association rule (it’s confidence is 1), then based on Intuitionistic fuzzy special sets (IFSS) Representation of Rough Set, IFSS rep...

Due to the increasing use of very large databases and data warehouses, mining useful information and helpful knowledge from transactions has become an important research area. Most conventional data-mining algorithms identify the relationships among transactions using binary values and find rules at a single concept level. Transactions with quantitative values and items with taxonomic relations...

In this paper, we present a novel technique, called F-APACS, for discovering fuzzy association rules in relational databases. Instead of dividing up quantitative attributes into fixed intervals and searching for rules expressed in terms of them, F-APACS employs linguistic terms to represent the revealed regularities and exceptions. The definitions of these linguistic terms are based on fuzzy se...

This paper introduces a new algorithm called User Association Rules Mining (UARM) for solving the problem of generating inadequate large number of rules in mining association technique using a fuzzy logic method [1, 2]. In order to avoid user’s defined threshold mistakes, the user has flexibility to determine constraints based on a set of features. In comparison with other well-known and widely...

Traditional approaches for mining generalized association rules are based only on database contents, and focus on exact matches among items. However, in many applications, the use of some background knowledge, as ontologies, can enhance the discovery process and generate semantically richer rules. In this way, this paper proposes the NARFO algorithm, a new algorithm for mining non-redundant and...

Data mining is most commonly used in attempts to induce association rules from transaction data. In the past, we used the fuzzy and GA concepts to discover both useful fuzzy association rules and suitable membership functions from quantitative values. The evaluation for fitness values was, however, quite time-consuming. Due to dramatic increases in available computing power and concomitant decr...

It is not an easy task to know a priori the most appropriate fuzzy sets that cover the domains of quantitative attributes for fuzzy association rules mining, simply because characteristics of quantitative data are in general unknown. Besides, it is unrealistic that the most appropriate fuzzy sets can always be provided by domain experts. Motivated by this, in this paperwe propose an automatedme...