Article history: Received 14 September 2008 Received in revised form 5 March 2010 Accepted 8 March 2010 Available online 15 March 2010 This paper presents the concept of temporal association rules in order to solve the problem of handling time series by including time expressions into association rules. Actually, temporal databases are continually appended or updated so that the discovered rule...

Association rule mining is a very important research topic in the field of data mining. Discovering frequent itemsets is the key process in association rule mining. Traditional association rule algorithms adopt an iterative method to discovery, which requires very large calculations and a complicated transaction process. Because of this, a new association rule algorithm called ABBM is proposed ...

In the present paper, we study iterative learning of indexable concept classes from noisy data. We distinguish between learning from positive data only and learning from positive and negative data; synonymously, learning from text and informant, respectively. Following 20], a noisy text (a noisy informant) for some target concept contains every correct data item innnitely often while in additio...

The paper presents a method of association rules discovering from medical data using the evolutionary approach. The elaborated method (EGAR) uses a genetic algorithm as a tool of knowledge discovering from a set of data, in the form of association rules. The method is compared with known and common method – FPTree. The developed computer program is applied for testing the proposed method and co...

Rule weights often have been used to improve the classification accuracy without changing the position of antecedent fuzzy sets. Recently, fuzzy versions of confidence and support merits from the field of data mining have been widely used for rules weighting in fuzzy rule based classifiers. This paper proposes an evolutionary approach for learning rule weights and uses more flexible equations, ...

Granularrules have been extensively used for classification in fuzzy datasets to promote the advancement of artificial intelligence. However, due diversity data types, how improve readability extracted granular rules while ensuring efficiency is always a challenge. Since reduct computing (GrC) can simplify real complex problem and dataset, this article carries out rule learning from perspective...

The design of fuzzy controllers for the implementation of behaviors in mobile robotics is a complex and highly time-consuming task. The use of machine learning techniques, such as evolutionary algorithms or artificial neural networks for the learning of these controllers allows to automate the design process. In this paper, the automated design of a fuzzy controller using genetic algorithms for...

General trust management model that we present is adapted for ad-hoc coalition environment, rather than for classic client-supplier relationship. The trust representation used in the model extends the current work by using the fuzzy number approach, readily representing the trust uncertainty without sacrificing the simplicity. The model contains the trust representation part, decision-making pa...

In this paper, a real-time iterative learning control (ILC) approach for a nonlinear continuous-time system using recurrent neural networks (RNN’s) with time-varying weights is presented. Two RNN’s are utilized in the ILC system. One is used to approximate the nonlinear system and another is used to mimic the desired system response. The ILC rule is obtained by combining the two RNN’s to form a...

Biclustering is a very useful data mining technique for identifying patterns where different genes are co-related based on a subset of conditions in gene expression analysis. Association rules mining is an efficient approach to achieve biclustering as in BIMODULE algorithm but it is sensitive to the value given to its input parameters and the discretization procedure used in the preprocessing s...