© 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...

Neuro-fuzzy systems have recently gained a lot of interest in research and application. These are approaches that learn fuzzy systems from data. Many of them use rule weights for this task. In this paper we discuss the innuence of rule weights on the interpretability of fuzzy systems. We show how rule weights can be equivalently replaced by modiications in the membership functions of a fuzzy sy...

In the approximate fuzzy reasoning the covering over of fuzzy rule base input and rule premise of a rule determines the importance of that fuzzy rule and the rule output as well. An axiom system has been created, describing the relationship between the fuzzy rule base system, rule input and rule output. By using distance-based operators a novel reasoning method appears by the compositional rule...

Wireless Sensor Networks (WSN) generates a huge amount of data for efficient application of discovering essential knowledge from it is important. Usually, WSN data is generated in streams and forwarded to a sink. Raw data leads to higher communication overhead adversely affecting WSN performance. Association mining processes data to locate frequent patterns. Thus, when association mining is app...

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...

In case of fuzzy reasoning in sparse fuzzy rule bases, the question of selecting the suitable fuzzy similarity measure is essential. The rule antecedents of the sparse fuzzy rule bases are not fully covering the input universe therefore fuzzy reasoning methods applied for sparse fuzzy rule bases requires similarity measures able to distinguish the similarity of non-overlapping fuzzy sets, too. ...

A neural network trading strategy optimized based on Apriori and the genetic algorithm is proposed to determine strategies. Using Neural Network Genetic Algorithm, we find all frequent term sets of fuzzy set mine association rules from set. Enter price dataset, minimum support multiplier; output rule library, iterate.

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...