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A Method for Estimating the Least Number of Objects in Fuzzy Clusters

Viattchenin D. A., Yaroma A.

International Journal of Electronics and Telecommunications

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2017

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Vol. 63, No. 4

341--346

EN

The theoretical note deals with the problem of estimation of the value of the least number of objects in fuzzy clusters for following detection of the optimal number of objects in fuzzy clusters through heuristic possibilistic clustering. A technique for detecting the optimal maximal number of elements in the a priori unknown number of fuzzy clusters of the sought clustering structure is reminded and a procedure for finding the initial minimal value of the number of objects in fuzzy clusters is proposed. Numerical examples are considered and conclusions are formulated.

2

Heuristic possibilistic clustering for detecting optimal number of elements in fuzzy clusters

Viattchenin D. A.

Foundations of Computing and Decision Sciences

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2016

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Vol. 41, No. 1

45--76

EN

The paper deals with the problem of discovering fuzzy clusters with optimal number of elements in heuristic possibilistic clustering. The relational clustering procedure using a parameter that controls cluster sizes is considered and a technique for detecting the optimal number of elements in fuzzy clusters is proposed. The effectiveness of the proposed technique is illustrated through numerical examples. Experimental results are discussed and some preliminary conclusions are formulated.

3

A direct algorithm of possibilistic clustering with partial supervision

Viattchenin D.

Journal of Automation Mobile Robotics and Intelligent Systems

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2007

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Vol. 1, No. 3

29-38

EN

Fuzzy clustering plays an important role in intelligent systems design and the respective methods constitute a part of the areas of automation and robotics. This paper describes a modification of a direct algorithm of possibilistic clustering that takes into account the information coming from the labeled objects. The clustering method based on the concept of allotment among fuzzy clusters is the basis of the new algorithm. The paper provides the description of basic ideas of the method and the plan of the basic version of a direct possibilistic-clustering algorithm. A plan of modification of the direct possibilistic-clustering algorithm in the presence of information from labeled objects is proposed. An illustrative example of the method's application to the Sneath and Sokal's two-dimensional data in comparison with the Gaussian-clustering method is carried out. Preliminary conclusions are formulated.

4

A new heuristic algorithm of fuzzy clustering

Viattchenin D. A.

Control and Cybernetics

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2004

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Vol. 33, no 2

323-340

EN

This paper deals with a new method of fuzzy clustering. The basic concepts of the method are introduced as resulting from the consideration of the fundamental fuzzy clustering problem. The paper provides the description of the general plan of the algorithm and an illustrative example. An analysis of the experimental results of the method's application to the Anderson's Iris data is carried out. Some preliminary conclusions and the ways of prospective investigations are given.