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1
Content available New developments in fuzzy clustering with emphasis on special types of tasks
Viattchenin D. A., Owsiński J. W., Kacprzyk J.
Control and Cybernetics
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2018
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Vol. 47, no. 2
115--130
EN
The paper is devoted to a survey of work done in fuzzy clustering, mainly during the first decade of the 21st century, and that with emphasis on various approachesto the problem, as well as various formulations of the very problem. That is why not only the classical formulations are treated, but several other problems, related to (the use of) clustering, like feature selection, inference systems, three-way clustering, and, on the other hand, such formulations of clustering as the possibilistic one or the one involving intuitionistic fuzzy sets. These are treated as the background for presentation of some specific ideas of the main author, concerning definite heuristic algorithms for effective solving of some of these problems.
2
Content available 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.
3
Content available remote 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.
4
Content available Extracting fuzzy classifications rules from three - way data
Kacprzyk J., Owsinski J. W., Viattchenin D. A.
Journal of Automation Mobile Robotics and Intelligent Systems
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2014
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Vol. 8, No. 2
47--57
EN
The paper deals in the conceptual way with the problem of extracting fuzzy classification rules from the three-way data meant in the sense of Sato and Sato [7]. A novel technique based on a heuristic method of possibilistic clustering is proposed. A description of basic concepts of a heuristic method of possibilistic clustering based on concept of an allotment is provided. A preprocessing technique for three-way data is shown. An extended method of constructing fuzzy classification rules based on clustering results is proposed. An illustrative example of the method’s application to the Sato and Sato’s data [7] is provided. An analysis of the experimental results obtained with some conclusions are given.
5
Content available A new heuristic possibilistic clustering algorithm for feature selection
Kacprzyk J., Owsinski J. W., Viattchenin D. A.
Journal of Automation Mobile Robotics and Intelligent Systems
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2014
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Vol. 8, No. 2
40--46
EN
The paper deals with the problem of selection of the most informative features. A new effective and efficient heuristic possibilistic clustering algorithm for feature selection is proposed. First, a brief description of basic concepts of the heuristic approach to possibilistic clustering is provided. A technique of initial data preprocessing is described and a fuzzy correlation measure is considered. The new algorithm is described and then illustrated on the well-known Iris data set benchmark and the results obtained are compared with those by using the conventional, well-known and widely employed method of principal component analysis (PCA). Conclusions and suggestions for future research are given.
6
Content available Automatic generation of fuzzy inference systems using heuristic possibilistic clustering
Viattchenin D. A.
Journal of Automation Mobile Robotics and Intelligent Systems
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2010
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Vol. 4, No. 3
36-44
EN
The interpretability and flexibility of fuzzy classification rules make them a popular basis for fuzzy controllers. Fuzzy control methods constitute a part of the areas of automation and robotics. The paper deals with the method of extracting fuzzy classification rules based on a heuristic method of possibilistic clustering. The description of basic concepts of the heuristic method of possibilistic clustering based on the allotment concept is provided. A general plan of the D-AFC(c)-algorithm is also given. A method of constructing and tuning of fuzzy rules based on clustering results is proposed. An illustrative example of the method's application to the Anderson's Iris data is carried out. An analysis of the experimental results is given and preliminary conclusions are formulated.
7
A methodology of fuzzy clustering with partial supervision
Viattchenin D. A.
Systems Science
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2007
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Vol. 33, no 4
61-71
EN
This paper describes a technique of fuzzy clustering with partial supervision. Pedrycz's algorithm of fuzzy clustering and a fuzzy clustering method based on the concept of allotment among fuzzy clusters form the basis of the technique. Basic ideas of both methods are considered and a methodology of fuzzy clustering with partial supervision is proposed in the paper. The application of the methodology is illustrated on the example of Anderson's Iris data. Preliminary conclusions are formulated.
8
Content available remote 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.
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