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...

In today’s life Intrusion Detection System gain the attention, because of ability to detect the intrusion access efficiently and effectively as security is the major issue in networks. This system identifies attacks and reacts by generating alerts or blocking the unwanted data/traffic. Intrusion Detection System mainly classified as Anomaly based intrusion detection systems that have benefit of...

The “Double Fuzzy Point” rule representation opens a new dimension for expressing changes of fuzziness in fuzzy rule-based systems. In the case of standard “Fuzzy Point” rule representations, it is difficult to describe fuzzy functions in which crisp observations are required to have fuzzy conclusions, or in which an increase in the fuzziness of observations leads to reduced fuzziness in conclu...

A temporal association rule is an association rule that holds during specific time intervals. An example can be that eggs and coffee are frequently sold together in morning hours. This paper studies temporal association rules during time intervals specified by user-given calendar schemas. Generally, the use of calendar schemas makes the discovered temporal association rules easier to understand...

This paper presents a novel boosting algorithm for genetic learning of fuzzy classification rules. The method is based on the iterative rule learning approach to fuzzy rule base system design. The fuzzy rule base is generated in an incremental fashion, in that the evolutionary algorithm optimizes one fuzzy classifier rule at a time. The boosting mechanism reduces the weight of those training in...

This paper presents an application of the novel theory of fuzzy networks for optimising models of systems characterised by uncertainty, non-linearity, modular structure and interactions. The application of the theory is demonstrated for retail price models in the context of converting a multiple rule base fuzzy system (MRBFS) into an equivalent single rule base fuzzy system (SRBFS) by linguisti...

the proposed iafc neural networks have both stability and plasticity because theyuse a control structure similar to that of the art-1(adaptive resonance theory) neural network.the unsupervised iafc neural network is the unsupervised neural network which uses the fuzzyleaky learning rule. this fuzzy leaky learning rule controls the updating amounts by fuzzymembership values. the supervised iafc ...

The inductive learning of fuzzy rule-based classification systems usually encounters an issue of exponential growth of the fuzzy rulesearch space when the number of patternsand/or variables becomes large.This issue makes the learning process more difficultand, in most cases, may lead to scalability problems. Alcalá-Fdezet al.proposed a fuzzy association rule-based classification method for high...

Designing an effective criterion for selecting the best rule is a major problem in theprocess of implementing Fuzzy Learning Classifier (FLC) systems. Conventionally confidenceand support or combined measures of these are used as criteria for fuzzy rule evaluation. In thispaper new entities namely precision and recall from the field of Information Retrieval (IR)systems is adapted as alternative...

Evolutionary Optimization is becoming omnipresent technique in almost every process of intelligent system design. Just to name few, engineering, control, economics and forecasting are some of the scientific fields that take advantage of an evolutionary computational process that aid in engineering systems with intelligent behavior. This special issue of Journal of Universal Computer Science is ...