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Distributed collaborative knowledge elicitation

Autorzy
Wybrane pełne teksty z tego czasopisma
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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
In this study, we develop an idea of knowledge elicitation realized over a collection of databases. The essence of such elicitation deals with a determination of common structure in databases. Depending upon a way in which databases are accessible abd can collaborate, we distinguish between a vertical and horizontal collaboration. In the first case, the databases deal with objects defined in the same attribute (feature) space. The horizontal collaboration takes place when dealing with the same objects but being defined in different attribute spaces and therefore forming separate databases. We develop a new clustering architecture supporting the mechanisms of collaboration. It is based on a standard FCM (Fuzzy C-Means) method. When it comes to the horizontal collaboration, the clustering algorithms interact by exchanging information about local partition matrices. In this sense, the required communication links are established at the level of information granules (more specifically, fuzzy sets forming the partition matrices) rather than patterns directly available in the databases. We discuss how this form of collaboration helps meet requirements of data confidentiality. In case of the horizontal collaboration, the method operates at the level of the prototypes formed for each individual database. Numeric examples are used to illustrate the method.
Słowa kluczowe
Rocznik
Strony
87--104
Opis fizyczny
Bibliogr. 12 poz., rys., tab., wykr.
Twórcy
autor
  • University of Alberta, Department of Electrical and Computer Engineering, Edmonton, Canada, T6R 2G7
Bibliografia
  • [1] M.R. Anderberg. Cluster Analysis for Applications. Academic Press, New York, 1973.
  • [2] J.C. Bezdek. Pałter Recognition with Fuzzy Objective Function Algorithms. Plenum Press, New York, 1981.
  • [3] R.N. Dave. Characterization and detection of noise in clustering. Pattern Recognition Letters, 12(11): 657-664, 1991.
  • [4] M. Delgado, F. Gomez-Skarmeta, F. Martin. A fuzzy clustering-based prototyping for fuzzy rule-based modeling. IEEE Transactions on Fuzzy Systems, 5(2): 223-233, 1997.
  • [5] R.O. Duda, P.E. Hart, D.G. Stork. Pattern Classification, 2nd edition. J. Wiley, New York, 2001.
  • [6] J.C. Dunn. A fuzzy relative of the ISODFATA process and its use in detecting compact well-separated clusters, J. of Cybernetics, 3(3): 32-57, 1973.
  • [7] F. Hoppner et al. Fuzzy Cluster Analysis. J. Wiley, Chichester, 1999. ł
  • [8] A. Kandel. Fuzzy Mathematical Techniques with Applications. Addison-Wesley, Reading, MA, 1986.
  • [9] P.R. Kersten. Fuzzy order statistics and their application to fuzzy clustering. IEEE Transacttons on Fuzzy Systems, 7(6): 708-712, 1999.
  • [10] W. Pedrycz. Fuzzy Sets Engineering. CRC Press, Boca Raton, FL, 1995.
  • [11] W. Pedrycz. Conditional fuzzy clustering in the design of radial basis function neural networks. IEEE Transactions on Neural Networks, 9(4): 601-612, 1996.
  • [12] M. Setnes. Supervised fuzzy clustering for rule extracticn. IEEE Transactions on Fuzzy Systems, 8(4): 416-424, 2000.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BPB2-0006-0055
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