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A new approach for the clustering using pairs of prototypes

Jezewski M., Czabanski R., Leski J., Horoba K.

Journal of Medical Informatics & Technologies

2015

Vol. 24

113--121

In the presented work two variants of the fuzzy clustering approach dedicated for determining the antecedents of the rules of the fuzzy rule-based classifier were presented. The main idea consists in adding additional prototypes (’prototypes in between’) to the ones previously obtained using the fuzzy c-means method (ordinary prototypes). The ’prototypes in between’ are determined using pairs of the ordinary prototypes, and the algorithm based on distances and densities finding such pairs was proposed. The classification accuracy obtained applying the presented clustering approaches was verified using six benchmark datasets and compared with two reference methods.

Classification problem in CBIR

Jaworska T.

Journal of Theoretical and Applied Computer Science

2013

Vol. 7, nr 2

3--15

At present a great deal of research is being done in different aspects of Content-Based Image Retrieval (CBIR). Image classification is one of the most important tasks in image retrieval that must be dealt with. The primary issue we have addressed is: how can the fuzzy set theory be used to handle crisp image data. We propose fuzzy rule-based classification of image objects. To achieve this goal we have built fuzzy rule-based classifiers for crisp data. In this paper we present the results of fuzzy rule-based classification in our CBIR. Furthermore, these results are used to construct a search engine taking into account data mining.