Association rule mining is concerned with the discovery of interesting association relationships hidden in databases. Existing algorithms typically assume that data characteristics are stable over time. Their main focus is therefore to mine association rules in an efficient manner. However, the world constantly changes. This makes the characteristics of real-life entities represented by the dat...

An efficient genetic reinforcement learning algorithm for designing fuzzy controllers is proposed in this paper. The genetic algorithm (GA) adopted in this paper is based upon symbiotic evolution which, when applied to fuzzy controller design, complements the local mapping property of a fuzzy rule. Using this Symbiotic-Evolution-based Fuzzy Controller (SEFC) design method, the number of control...

This work focuses on the formalization of a Fuzzy Cognitive Map based decision support system using fuzzy If-Then rules (extracted from data) accompanied with the available experts’ knowledge. The proposed approach is applied to build a Fuzzy Cognitive Map (FCM) grading tool, an advanced FCM-based model used for prediction. The FCM is a modeling methodology based on exploiting knowledge and exp...

Ontologies have been successfully employed in applications that require semantic information processing. However, traditional ontologies are not able to express fuzzy or vague information, which often occurs in human vocabulary as well as in several application domains. In order to deal with such restriction, concepts of fuzzy set theory should be incorporated into ontologies so that it is poss...

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...

In this paper, we design fuzzy sliding mode controller by real-value genetic algorithms. In the process of designing the proposed controller, we firstly employ the sliding mode control technique to design the fuzzy rules, so that the fuzzy controller has the characteristics of stability and robustness. Next, we adopt the Takagi-Sugeno’s fuzzy mode to construct the fuzzy control rules and to obt...

This paper examines the effect of rule weights in fuzzy rule-based classification systems. Each fuzzy IF–THEN rule in our classification system has antecedent linguistic values and a single consequent class. We use a fuzzy reasoning method based on a single winner rule in the classification phase. The winner rule for a new pattern is the fuzzy IF–THEN rule that has the maximum compatibility gra...

Lee, Stolfo, and Mok have previously reported the use of association rules and frequency episodes for mining audit data to gain knowledge for intrusion detection. The integration of association rules and frequency episodes with fuzzy logic can produce more abstract and flexible patterns for intrusion detection, since many quantitative features are involved in intrusion detection and security it...

In Web usage mining, fuzzy association rules that have a temporal property can provide useful knowledge about when associations occur. However, there is a problem with traditional temporal fuzzy association rule mining algorithms. Some rules occur at the intersection of fuzzy sets’ boundaries where there is less support (lower membership), so the rules are lost. A genetic algorithm (GA)-based s...