Extracting fuzzy classifications rules from three - way data

• Autorzy: Kacprzyk J. , Owsinski J. W. , Viattchenin D. A.

Identyfikatory

DOI

10.14313/JAMRIS_2-2014/19

• Języki publikacji: EN

Abstrakty

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.

Słowa kluczowe

EN

three-way data; possibilistic clustering; fuzzy cluster; typical point; fuzzy rule

• Wydawca: Łukasiewicz Industrial Research Institute for Automation and Measurements PIAP
• Czasopismo: Journal of Automation Mobile Robotics and Intelligent Systems
• Rocznik: 2014
• Tom: Vol. 8, No. 2
• Strony: 47--57
• Opis fizyczny: Bibliogr. 16 poz., rys.

Twórcy

autor

Kacprzyk J.

• Systems Research Institute, Polish Academy of Sciences, 6 Newelska St., 01-447 Warsaw, Poland

autor

Owsinski J. W.

• Systems Research Institute, Polish Academy of Sciences, 6 Newelska St., 01-447 Warsaw, Poland

autor

Viattchenin D. A.

• United Institute of Informatics Problems, National Academy of Sciences of Belarus, 6 Surganov St., 220012 Minsk, Belarus

Bibliografia

• [1] Bezdek J.C., Pattern Recognition with Fuzzy Objective Function Algorithms, Plenum Press, New York, 1981.DOI: http://dx.doi.org/10.1007/978-1-4757-0450-1
• [2] Höppner F., Klawonn F., Kruse R., Runkler T., Fuzzy Cluster Analysis: Methods for Classification, Data Analysis and Image Recognition, Wiley, Chichester, 1999.
• [3] Kacprzyk J., Multistage Fuzzy Control, Wiley, Chichester, 1997.
• [4] Krishnapuram R., Keller J.M., “A possibilistic approach to clustering”, IEEE Trans. on Fuzzy Syst., no. 1, 1993, 98–110. DOI: http://dx.doi.org/10.1109/91.227387
• [5] Mamdani E.H., Assilian S., “An experiment in linguistic synthesis with a fuzzy logic controller”, International Journal of Man-Machine Studies, vol. 7, no. 1, 1975, 1–13. DOI: http://dx.doi.org/10.1016/S0020-7373(75)80002-2
• [6] Mandel I.D., Clustering Analysis, Finansy i Statistica, Moscow, 1988. (in Russian)
• [7] Sato M., Sato Y., “On a multicriteria fuzzy clustering method for 3-way data”, International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, vol. 2, no. 2, 1994, 127–142. DOI: http://dx.doi.org/10.1142/S0218488594000122
• [8] Viattchenin D.A., “A new heuristic algorithm of fuzzy clustering”, Control & Cybernetics, vol. 33, 2004,323–340.
• [9] Viattchenin D.A., “A direct algorithm of possibilistic clustering with partial supervision”, Journal of Automation,Mobile Robotics and Intelligent Systems, vol. 1, no. 3, 2007, 29–38.
• [10] Viattchenin D.A., “Direct algorithms of fuzzy clustering based on the transitive closure operation and their application to outliers detection”, Artificial Intelligence, vol. 3, 2007, 205–216. (in Russian)
• [11] Viattchenin D.A., “An outline for a heuristic approach to possibilistic clustering of the three-way data”, Journal of Uncertain Systems, vol. 3, 2009, 64–80.
• [12] Viattchenin D.A., “An algorithm for detecting the principal allotment among fuzzy clusters and its application as a technique of reduction of analyzed features space dimensionality”, Journal of Information and Organizational Sciences, vol. 33, no. 1, 2009, 205–217.
• [13] Viattchenin D.A., Automatic generation of fuzzy inference systems using heuristic possibilistic clustering, Journal of Automation, Mobile Robotics and Intelligent Systems, vol. 4, no. 3, 2010, 36–44.
• [14] Viattchenin D.A., “Derivation of fuzzy rules from interval-valued data”, International Journal of Computer Applications, vol. 7, no. 3, 2010, 13–20. DOI: http://dx.doi.org/10.5120/1146-1500
• [15] Viattchenin D.A., “Validity measures for heuristic possibilistic clustering”, Information Technology and Control, vol. 39, 2010, 321–332.
• [16] Viattchenin D.A., “Constructing stable clustering structure for uncertain data set”, Acta Electrotechnica et Informatica, vol. 11, no. 3, 2011, 42–50. DOI:http://dx.doi.org/10.2478/v10198-011-0028-5