Fuzzy Sets and Systems

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 ...

Partitioning a time-series into internally homogeneous segments is an important data-mining problem. The changes of the variables of a multivariate time-series are usually vague and do not focus on any particular time point. Therefore, it is not ...

In knowledge discovery, Genetic Algorithms have been used for classification, model selection, and other optimization tasks. However, behavior and performance of genetic algorithms are directly affected by the values of their input parameters, while ...

The aim of this paper is to provide a crystal clear insight into the true semantics of the measures of support and confidence that are used to assess rule quality in fuzzy association rule mining. To achieve this, we rely on two important pillars: the ...

In this paper a neuro-fuzzy modeling framework is proposed, which is devoted to discover knowledge from data and represent it in the form of fuzzy rules. The core of the framework is a knowledge extraction procedure that is aimed to identify the ...

In the analysis of data stored in databases, a very interesting issue is the detection of possible existing relations between attribute values and, at an upper level, relations between attributes themselves. In case uncertainty is present in data, or it ...

This paper examines the measurement of the degree to which tuples in a database support a relation among attributes based on a comparison of the number of examples and counterexamples of the relation. In particular, we are concerned with associations ...

Feature selection refers to the problem of selecting those input features that are most predictive of a given outcome; a problem encountered in many areas such as machine learning, pattern recognition and signal processing. In particular, solution to ...

Recently, semi-supervised learning has received quite a lot of attention. The idea of semi-supervised learning is to learn not only from the labeled training data, but to exploit also the structural information in additionally available unlabeled data. ...

A new scheme based on multi-objective hierarchical genetic algorithm (MOHGA) is proposed to extract interpretable rule-based knowledge from data. The approach is derived from the use of multiple objective genetic algorithm (MOGA), where the genes of the ...

We describe an interactive method to generate a set of fuzzy clusters for classes of interest of a given, labeled data set. The presented method is therefore best suited for applications where the focus of analysis lies on a model for the minority class ...