A significant data mining issue is the effective discovery of association rules. The extraction of association rules faces the problem of the combinatorial explosion of the search space, and the loss of information by the discretizat ion of values. The first problem is confronted effectively by the Frequent Pattern Tree approach of [10 ]. This approach avoids the candidate generation phase of A...

The use of hierarchical taxonomies to organise information (or sets of objects) is essential to the semantic web and is also fundamental to many aspects of web 2.0. In most cases, the seemingly crisp granulation of a taxonomy disguises the fact that categories are based on loosely defined concepts which are better modelled by allowing graded membership. Fuzzy categories may also arise when inte...

A significant data mining issue is the effective discovery of association rules. The extraction of association rules faces the problem of the combinatorial explosion of the search space, and the loss of information by the discretization of values. The first problem is confronted effectively by the Frequent Pattern Tree approach of [10]. This approach avoids the candidate generation phase of Apr...

Many methods have been proposed to mine fuzzy association rules from databases with crisp values in order to help decision-makers make good decisions and tackle new types of problems. However, most real-world problems present a certain degree of imprecision. Various studies have been proposed to mine fuzzy association rules from imprecise data but they assume that the membership functions are k...

This paper presents a method for deriving Association rules by using apriori algorithm, clustering and fuzzy set concepts. Association rules of quantitative data are presented with mean and standard deviation, and with fuzzy linguistic terms. A case study was done on the commodity data to demonstrate vitality of proposed method. The statistical and fuzzy Association rules, inferred from the com...

Intrusion Detection is one of the important area of research. Our work has explored the possibility of integrating the fuzzy logic with Data Mining methods using Genetic Algorithms for intrusion detection. The reasons for introducing fuzzy logic is two fold, the first being the involvement of many quantitative features where there is no separation between normal operations and anomalies. Thus f...

A novel method for mining association rules that are both quantitative and temporal using a multi-objective evolutionary algorithm is presented. This method successfully identifies numerous temporal association rules that occur more frequently in areas of a dataset with specific quantitative values represented with fuzzy sets. The novelty of this research lies in exploring the composition of qu...

© 2014. The Authors. Published under Afro Asian Journal of Science and Technology www.aajst.com 26 Amir Ebrahimzadeh Sama technical and vocational training college, Islamic Azad University, Mashhad branch, Mashhad, Iran Abstract due to increasing use of huge databases , mining practical information and useful knowledge from transactions is evolving into an important area. Most data mining metho...

Association rules shows us interesting associations among data items. It means that an association rule clearly defines that how a data item is related or associated with another data item. That is why these types of rules are called Association rules. And the procedure by which these rules are extracted and managed is known as Association rule mining. Classical association rule mining had many...

Fuzzy association rule mining discovers patterns in transactions, such as shopping baskets in a supermarket, or Web page accesses by a visitor to a Web site. Temporal patterns can be present in fuzzy association rules because the underlying process generating the data can be dynamic. However, existing solutions may not discover all interesting patterns because of a previously unrecognised probl...