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A direct algorithm of possibilistic clustering with partial supervision

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EN
Abstrakty
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.
Twórcy
  • United Institute of Informatics Problems of the National Academy of Sciences of Belarus, Surganov St. 6, 220012 Minsk, Belarus, viattchenin@mail.ru
Bibliografia
  • [1] Bensaid A.M., Hall L.O., Bezdek J.C., and Clarke L.P., "Partially supervised clustering for image segmentation",Pattern Recognition , vol. 29, no. 5, 1996, pp. 859-871.
  • [2] Bouchachia A. and Pedrycz W., "Enhancement of fuzzy clustering by mechanisms of partial supervision", Fuzzy Sets and Systems, vol. 157, issue 13, 2006, pp. 1733-1759.
  • [3] Davé R.N., "Use of the adaptive fuzzy clustering algorithm to detect lines in digital images",Intelligent Robots and Computer Vision, 1989, vol. 1192, pp. 600-611.
  • [4] Höppner F., Klawonn F., Kruse R. and Runkler T.,Fuzzy Cluster Analysis: Methods for classification, data analysis and image recognition, Chichester: Wiley Intersciences, 1998.
  • [5] Krishnapuram R. and Keller J.M., "A Possibilistic Approach to Clustering",IEEE Transactions on Fuzzy Systems, vol. 1, 1993, pp. 98-110.
  • [6] Łęski J.M., "Robust possibilistic clustering",Archives of Control Sciences, vol. 10, 2000, pp. 141-155.
  • [7] Li R. and Mukaidono M., "Gaussian clustering method based on maximum-fuzzy-entropy interpretation",Fuzzy Sets and Systems, vol. 102, 1999, pp. 253-258.
  • [8] Mandel I.D.,Clustering analysis , Moscow: "Finansy i Statistica" Publishing House, 1988, (in Russian).
  • [9] Pedrycz W., "Algorithms of fuzzy clustering with partial supervision",Pattern Recognition Letters , vol. 3, 1985,pp. 13-20.
  • [10] Pedrycz W., "Fuzzy sets in pattern recognition: methodology and methods", Pattern Recognition, vol. 23, 1990, pp. 121-146.
  • [11] Sneath P.H.A. and Sokal R., Numerical Taxonomy, San Francisco: Freeman, 1973.
  • [12] Viattchenin D.A., "On Projections of Fuzzy Similarity Relations", Proc. of the 5th International Conference on Computer Data Analysis and Modeling CDAM'1998, Minsk, Belarus, 2003, vol. 2, pp. 91-94.
  • [13] Viattchenin D.A., "Remarks on Kinds of Fuzzy Clusters", Proc. of the International Conference “Statistical Data Analysis of Quality of Life”, Wroclaw, Poland, 1999, pp. 69-79.
  • [14] Viattchenin D.A., "Criteria of Quality of Allotment in Fuzzy Clustering", Proc. of the 3rd International Conference on Neural Networks and Artifical Intelligence ICNNAI'2003, Minsk, Belarus, 2003, pp. 91-94.
  • [15] Viattchenin D.A., Fuzzy methods of automatic classification , Minsk: Technoprint Publishing House, 2004, (in Russian).
  • [16] Viattchenin D.A., "A new heuristic algorithm of fuzzy clustering", Control & Cybernetics, vol. 33, 2004, pp. 323-340.
  • [17] Viattchenin D.A., "Parameters of the AFCmethod of fuzzy clustering", Bulletin of The Military Academy of The Republic of Belarus, no. 4, 2004, pp. 51-55, (in Russian).
  • [18] Yang M.-S. and Wu K.-L., "Unsupervised possibilistic clustering",Pattern Recognition, 2006, vol. 39, pp. 5-21.
  • [19] Zadeh L.A., "Fuzzy Sets", Information and Control, vol. 8, 1965, pp. 338-353.
  • [20] Zhang J.-S. and Leung Y.-W., "Improved Possibilistic C-Means Clustering Algorithms", IEEE Transactions on Fuzzy Systems, 2004, vol. 12, pp. 209-217.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BUJ6-0014-0020
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