Data mining is the analysis step of the "Knowledge Discovery in Databases" process, or KDD. It is the process that results in the discovery of new patterns in large data sets. It utilizes methods at the intersection of artificial intelligence, machine learning, statistics, and database systems. The overall goal of the data mining process is to extract knowledge from an existing data set and tra...

Pascual Juli an a Gin es Moreno b Jaime Penabad c aDepartment of Computer Science ESI, Univ. of Castilla{La Mancha Paseo de la Universidad, 4; 13071 Ciudad Real, Spain bDepartment of Computer Science cDepartment of Mathematics EPSA, Univ. of Castilla{La Mancha Campus Universitario, s/n; 02071 Albacete, Spain Abstract In the context of (fuzzy) logic programs, `unfolding' means to transform a pro...

A fuzzy rule-based system consists of fuzzy if-then rules such as “If x1 is small and x2 is small than y is large”. The problem with existing fuzzy rule-based systems is that the size of the rule-base (number of rules) increases exponentially with the increase of the number of fuzzy sets involved in the rules. This exponential increase in size of the rule-base increases the search time and henc...

In this study, an on-line tuning method is proposed for fuzzy PID controllers via rule weighing. The rule weighing mechanism is a fuzzy rule base with two inputs namely; ‘‘error’’ and ‘‘normalized acceleration’’. Here, the normalized acceleration provides relative information on the fastness or slowness of the system response. In deriving the fuzzy rules of the weighing mechanism, the transient...

The purpose of the work described in this paper is to provide an intelligent intrusion detection system (IIDS) that uses two of the most popular data mining tasks, namely classification and association rules mining together for predicting different behaviors in networked computers. To achieve this, we propose a method based on iterative rule learning using a fuzzy rule-based genetic classifier....

Systems applying fuzzy logic are rule based ones. The collection of the rules the so called rule base can be characterized as dense or sparse depending on whether there exist rules for all the possible observations. In the sparse case for some observations there are no rules whose antecedent part would overlap the observation at least partially. Therefore the classical compositional reasoning m...

In this paper we introduce a fuzzy version of symport/antiport membrane systems. Our fuzzy membrane systems handle possibly inexact copies of reactives and their rules are endowed with threshold functions that determine whether a rule can be applied or not to a given set of objects, depending of the degree of accuracy of these objects to the reactives specified in the rule. We prove that these ...

In support of reasoning with sparse rule bases, fuzzy rule interpolation (FRI) offers a helpful inference mechanism for deriving an approximate conclusion when a given observation has no overlap with any rule in the existing rule base. One of the recent and popular FRI approaches is the scale and move transformation-based rule interpolation, known as T-FRI in the literature. It supports both in...

Images of fuzzy sets under fuzzy relations have been investigated mainly in two contexts: on the one hand, mostly under the term “full image” (Gottwald, 1993), they can be regarded as very general tools for fuzzy inference, leading to the so-called “compositional rule of inference” (Gottwald, 1993; Bauer et al., 1995). The theory of fuzzy relational equations makes direct use of this fundamenta...