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A methodology of fuzzy clustering with partial supervision

Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
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.
Czasopismo
Rocznik
Strony
61--71
Opis fizyczny
Bibliogr. 15 poz., rys.
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] Bellman R., Kalaba R., Zadeh L.A., Abstraction and Pattern Classification, Journal of Mathematical Analysis and Applications, Vol. 13, 1966, pp. 1-7.
  • [2] Höppner F., Klawonn F., Kruse R., Runkler T., Fuzzy Cluster Analysis: Methods for classification, data analysis and image recognition, Wiley Intersciences, Chichester, 1999.
  • [3] Viattchenin D.A., A new heuristic algorithm of fuzzy dustering, Control & Cybernetics, Vol. 33, 2004, pp. 323-340.
  • [4] Dave R.N., Use of the adaptive fuzzy clustering algorithm to detect lines in digital images, Intelligent Robots and Computer Vision VIII, Vol. 1192, 1989, pp. 600-611.
  • [5] Viattchenin D.A., Parameters of the AFC-method of fuzzy clustering, Bulletin of The Military Academy of The Republic of Belarus, No. 4, 2004, pp. 51-55, (in Russian).
  • [6] Pedrycz W., Algorithms of fuzzy clustering with partial supervision. Pattern Recognition Letters, Vol. 3, 1985, pp. 13-20.
  • [7] Pedrycz W., Fuzzy sets in pattern recognition: methodology and methods, Pattern Recognition, Vol. 23, 1990, pp. 121-146.
  • [8] Dunn J.C., A fuzzy relative of the ISODATA process and its use in delecting compact well-separated clusters, Journal of Cybernetics, Vol. 3, 1974, pp. 32-57.
  • [9] Bezdek J.C., Pattern Recognition with Fuzzy Objective Function Algorithms, Plenum Press, New York, 1981.
  • [10] Viattchenin D.A., Fuzzy methods of automatic classification, Technoprint Publishing House, Minsk, 2004, (in Russian).
  • [11] Viattchenin D.A., Remarks on Kinds of Fuzzy Clusters, Proc. Int. Conf. Statistical Data Analysis of Quality of Life, Wrocław University of Economics, Wrocław, Poland, 1999, pp. 69-79.
  • [12] Viattchenin D.A., Criteria of Quality of Allotment in Fuzzy Clustering, Proc. Third Int. Conf. Neural Networks and Artificial Intelligence, Belarusian State University of Informatics and Radioelectronics, Minsk, Belarus, 2003, pp. 91-94.
  • [13] Kaufmann A., Introduction to the Theory of Fuzzy Subsets, Vol. 1, Academic Press, New York, 1975.
  • [14] Anderson E., The irises of the Gaspe peninsula. Bulletin of the American Iris Society, Vol. 59, 1934, pp. 2-5.
  • [15] Viattchenin D.A., On the Inspection of Classification Results in the Fuzzy Clustering Method Based on the Allotment Concept, Proc. Fourth Int. Conf. Neural Networks and Artificial Intelligence, Brest State Technical University, Brest, Belarus, 2006, pp. 210-216.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BAT5-0033-0039
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