Collaborative Architectures of Fuzzy Modeling

  • Witold Pedrycz
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5050)

Abstract

There are evident and profoundly articulated needs to deal with distributed sources of data (such as e.g., sensors and sensor networks, web sites, distributed databases). While recognizing limited accessibility of such data at a global level (which could be associated with technical constraints and/or privacy issues) and fully acknowledging benefits and potentials of collaborative processing, we introduce a concept of Collaborative Computational Intelligence (CI), and collaborative fuzzy models, in particular. Collaboration is realized in different ways by engaging a host of bidirectional interactions between all local processing sites (models) or by proceeding with unidirectional communication in which we establish some mechanisms of developing experience consistency of fuzzy modeling. We offer a coherent taxonomy of various schemes of interaction which in the sequel implies a certain development of a suite of algorithms. In this setting, we highlight a pivotal role of granular information in the establishing of the mechanisms of interaction. In the realm of collaborative fuzzy models and fuzzy modeling we elaborate on the concept of knowledge sharing. We also bring forward a concept of experience–consistent fuzzy system identification showing how fuzzy models built on a basis of limited data can benefit from taking advantage of the past experience conveyed in the form of previously constructed fuzzy models. Proceeding with a more detailed algorithmic framework, we elaborate on the key design issues concerning fuzzy rule-based systems which constitute a dominant category of fuzzy models. Collaboration invokes some mechanisms of aggregation and reconciliation of local findings. We emphasize that the resulting findings such as specific components of models can be quantified in terms of type-2 fuzzy sets – a pursuit which offers an interesting motivation behind this higher type of fuzzy sets.

Keywords

distributed Computational Intelligence fuzzy sets fuzzy models information granules collaboration 

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References

  1. 1.
    Acampora, G., Loia, V.: A Proposal of Ubiquitous Fuzzy Computing for Ambient Intelligence. Information Sciences 178, 631–646 (2008)CrossRefGoogle Scholar
  2. 2.
    Bezdek, J.C.: Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum Press, New York (1981)MATHGoogle Scholar
  3. 3.
    Cheng, C.B., Chan, C.C.H., Lin, K.C.: Intelligent Agents for e-Marketplace: Negotiation with Issue Trade-Offs by Fuzzy Inference Systems. Decision Support Systems 42, 626–638 (2006)CrossRefGoogle Scholar
  4. 4.
    Costa da Silva, J., Klusch, M.: Inference in Distributed Data Clustering. Engineering Applications of Artificial Intelligence 19, 363–369 (2006)CrossRefGoogle Scholar
  5. 5.
    Dudoit, S., Fridlyand, J.: Bagging to Improve the Accuracy of a Clustering Procedure. Bioinformatics 19, 1090–1099 (2003)CrossRefGoogle Scholar
  6. 6.
    Dimitriadou, A., Weingessed, K., Hornik, K.: Voting-Merging: An Ensemble Method for Clustering. In: Proc. Int. Conf. on Artificial Neural Networks, Vienna, pp. 217–222 (2001)Google Scholar
  7. 7.
    Genesereth, M.R., Ketchpel, S.P.: Software Agents. Communications of the ACM 37, 48–53 (1994)CrossRefGoogle Scholar
  8. 8.
    Hubert, L., Arabie, P.: Comparing Partitions. Journal of Classification 2, 193–218 (1985)CrossRefGoogle Scholar
  9. 9.
    Hoppner, F., et al.: Fuzzy Cluster Analysis. J. Wiley, Chichester (1999)Google Scholar
  10. 10.
    Jain, A., Murt, M., Flynn, P.: Data Clustering: a Review. ACM Computing Surveys 31, 264–323 (1999)CrossRefGoogle Scholar
  11. 11.
    Jain, A.K., Dubes, R.C.: Algorithms for Clustering Data. Prentice-Hall, Englewood Cliffs (1988)MATHGoogle Scholar
  12. 12.
    Johnson, E., Kargupta, H.: Collective, Hierarchical Clustering from Distributed, Heterogeneous Data. In: Zaki, M.J., Ho, C.-T. (eds.) KDD 1999. LNCS (LNAI), vol. 1759, pp. 221–244. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  13. 13.
    Krogh, A., Vedelsby, J.: Neural Networks Ensembles, Cross Validation, and Active Learning. In: Advances in Neural Information Processing Systems, pp. 231–238. MIT Press, Cambridge (1995)Google Scholar
  14. 14.
    Leung, Y., Zhang, J., Xu, Z.: Clustering by Space-Space Filtering. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 1396–1410 (2000)CrossRefGoogle Scholar
  15. 15.
    Loia, V., Pedrycz, W., Senatore, S.: P-FCM: a Proximity-Based Fuzzy Clustering for User-Centered Web Applications. Int. J. of Approximate Reasoning 34, 121–144 (2003)MATHCrossRefGoogle Scholar
  16. 16.
    Merugu, S., Ghosh, J.: A Privacy-Sensitive Approach to Distributed Clustering. Pattern Recognition Letters 26, 399–410 (2005)CrossRefGoogle Scholar
  17. 17.
    Nowak, R.: Distributed EM Algorithms for Density Estimation and Clustering in Sensor Networks. IEEE Trans. on Signal Processing 51, 2245–2253 (2003)CrossRefGoogle Scholar
  18. 18.
    Oehler, K.L., Gray, R.M.: Combining Image Compression and Classification Using Vector Quantization. IEEE Transactions on Pattern Analysis and Machine Intelligence 17, 461–473 (1995)CrossRefGoogle Scholar
  19. 19.
    Pedrycz, W., Vukovich, G.: Clustering in the Framework of Collaborative Agents. In: Proc. 2002 IEEE Int. Conference on Fuzzy Systems, vol. 1, pp. 134–138 (2002)Google Scholar
  20. 20.
    Pedrycz, W.: Collaborative fuzzy clustering. Pattern Recognition Letters 23, 675–686 (2002)Google Scholar
  21. 21.
    Pedrycz, W.: Knowledge-Based Clustering: From Data to Information Granules. J. Wiley, Chichester (2005)MATHGoogle Scholar
  22. 22.
    Pedrycz, W., Rai, P.: Collaborative Clustering with the Use of Fuzzy C-Means and Its Quantification. Fuzzy Sets & Systems (to appear)Google Scholar
  23. 23.
    Strehl, A., Ghosh, J.: Cluster ensembles: a Knowledge Reuse Framework for Combining Multiple Partitions. Journal of Machine Learning Research 3, 583–617 (2002)CrossRefMathSciNetGoogle Scholar
  24. 24.
    Topchy, A., Jain, K., Punch, W.: Clustering ensembles: models of consensus and weak partitions. IEEE Transactions on Pattern Analysis and Machine Intelligence 27, 1866–1881 (2005)CrossRefGoogle Scholar
  25. 25.
    Wiswedel, B., Berthold, M.R.: Fuzzy Clustering in Parallel Universes. J. of Approximate Reasoning 45, 439–454 (2007)MATHCrossRefGoogle Scholar
  26. 26.
    Zadeh, L.A.: Towards a Theory of Fuzzy Information Granulation and its Centrality in Human Reasoning and Fuzzy Logic. Fuzzy Sets and Systems 90, 111–117 (1997)MATHCrossRefMathSciNetGoogle Scholar
  27. 27.
    Zadeh, L.A.: Toward a generalized theory of Uncertainty (GTU)-—an Outline. Information Sciences 172, 1–40 (2005)MATHCrossRefMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Witold Pedrycz
    • 1
  1. 1.Department of Electrical & Computer Engineering, University of Alberta, Edmonton, T6R 2G7 Canada and, Systems Research Institute Polish Academy of Sciences, WarsawPoland

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