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New developments in fuzzy clustering with emphasis on special types of tasks

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
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.
Rocznik
Strony
115--130
Opis fizyczny
Bibliogr. 32 poz., rys., tab.
Twórcy
  • Systems Research Institute, Polish Academy of Sciences Newelska 6, 01-447 Warszawa, Poland
autor
  • Systems Research Institute, Polish Academy of Sciences Newelska 6, 01-447 Warszawa, Poland
Bibliografia
  • [1] Anderson E. (1935) The irises of the Gaspe Peninsula. Bulletin of the American Iris Society, 59 (1): 2-5.
  • [2] Bezdek J. C. (1981) Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum Press, New York.
  • [3] Bezdek J. C., Keller J. M., Krishnapuram R. and Pal N. R. (2005) Fuzzy Models and Algorithms for Pattern Recognition and Image Processing. Springer Science, New York.
  • [4] Blum A. and Langley P. (1997) Selection of relevant features and examples in machine learning. Artificial Intelligence, 97 (1-2), 245-271.
  • [5] Chaudhuri B. B. and Bhattacharya A. (2001) On correlation between two fuzzy sets. Fuzzy Sets and Systems 118 (3), 447-456.
  • [6] Chiang D.-A. and Lin N. P. (1999) Correlation of fuzzy sets. Fuzzy Sets and Systems 102 (2), 221-226.
  • [7] Chitsaz E., Taheri M. and Katebi S. D. (2008) A fuzzy approach to clustering and selecting features for classification of gene expression data. Proc. World Congress of Engineering (WCE’2008), International Asssociation of Engineers, 1650-1655 http://www.iaeng.org/publication/WCE2008/
  • [8] Chitsaz E., Taheri M., Katebi S. D. and Jahromi M. Z. (2009) An improved fuzzy feature clustering and selection based on chi-squaredtest. Proc. Int. Multiconference of Engineers and Computer Scientists (IMECS’2009). International Association of Engineers, 35-40 http://www. iaeng.org/publication/WCE2008/
  • [9] Draminski M., Kierczak M., Nowak-Brzezinska A., Koronacki J. and Komorowski J. (2011) The Monte Carlo feature selection and interdependency discovery is unbiased. Control and Cybernetics, 40 (2), 199-211.
  • [10] Ghazavi S. N. and Liao T. W. (2008) Medical data mining by fuzzy modeling with selected features. Artificial Intelligence in Medicine, 43 (3), 195-206.
  • [11] Hoppner F., Klawonn F., Kruse R. and Runkler T. (1999) Fuzzy Cluster Analysis: Methods for Classification, Data Analysis and Image Recognition. Wiley, Chichester.
  • [12] Kacprzyk J. (1997) Multistage Fuzzy Control. Wiley, Chichester.
  • [13] Kohavi R. and John G. (1997) Wrappers for feature subset selection. Artificial Intelligence, 97 (1-2), 273-324.
  • [14] Kong Y.-Q. and Wang S.-T. (2009) Feature selection and semi-supervised fuzzy clustering. Fuzzy Information and Engineering 1 (2), 179-190.\
  • [15] Krishnapuram R. and Keller J. M. (1993) A possibilistic approach to clustering. IEEE Trans. on Fuzzy Systems, 1 (2), 98-110.
  • [16] Mamdani E. H. and Assilian S. (1975) An experiment in linguistic synthesis with a fuzzy logic controller. International Journal of Man-Machine Studies 7, 1-13.
  • [17] Mandel I. D. (1988) Clustering Analysis. Finansy i Statistika, Moscow (in Russian).
  • [18] Murthy C. A., Pal S. K. and Dutta Majumder D. (1985) Correlation between two fuzzy membership functions. Fuzzy Sets and Systems 17 (1), 23-38.
  • [19] Ruspini E. H. (1973) New experimental results in fuzzy clustering. Information Sciences, 6, 273-284.
  • [20] Sato M. and Sato Y. (1994) On a multicriteria fuzzy clustering method for 3way data. International Journal of Uncertainty, Fuzziness and KnowledgeBased Systems 2, 127-142.
  • [21] Sato-Ilic M. and L.C. Jain L. C. (2006) Innovations in Fuzzy Clustering: Theory and Applications. Springer-Verlag, Heidelberg.
  • [22] Viattchenin D. A. (2004) A new heuristic algorithm of fuzzy clustering. Control and Cybernetics 33 (2), 323-340.
  • [23] Viattchenin D. A. (2007a) A direct algorithm of possibilistic clustering with partial supervision. Journal of Automation, Mobile Robotics and Intelligent Systems 1 (3), 29-38.
  • [24] Viattchenin D. A. (2007b) Direct algorithms of fuzzy clustering based on the transitive closure operation and their application to outliers detection. Artificial Intelligence 3, 205-216 (in Russian).
  • [25] Viattchenin D. A. (2009a) An algorithm for detecting the principal allotment among fuzzy clusters and its application as a technique of reduction of analyzed features space dimensionality. J. Information and Organizational Sciences 33 (1), 205-217.
  • [26] Viattchenin D. A. (2009b) An outline for a heuristic approach to possibilistic clustering of the three-way data. Journal of Uncertain Systems 3, 64-80.
  • [27] Viattchenin D. A. (2010a) Automatic generation of fuzzy inference systems using heuristic possibilistic clustering. Journal of Automation, Mobile Robotics and Intelligent Systems 4(3) 36-44.
  • [28] Viattchenin D. A. (2010b) Derivation of fuzzy rules from interval-valued data. International Journal of Computer Applications 7(3) 13-20.
  • [29] Viattchenin D. A. (2010c) Validity measures for heuristic possibilistic clustering. Information Technology and Control 39, 321-332.
  • [30] Viattchenin D. A. (2011) Constructing stable clustering structure for uncertain data set. Acta Electrotechnica et Informatica 11(3) 42-50.
  • [31] Walesiak M. (2002) A generalized distance measure in statistical multivarate analysis., Wydawnictwo Akademii Ekonomicznej im. Oskara Langego, Wroclaw (in Polish).
  • [32] Zadeh L. (1965) Fuzzy sets. Information and Control; 8, 338–353.
Uwagi
PL
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-2b929615-eb72-4167-aa5b-f3ce3c65599f
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