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International Conference on Distributed Computing and Networking

ICDCN 2008: Distributed Computing and Networking pp 1–12Cite as

Rough-Fuzzy Knowledge Encoding and Uncertainty Analysis: Relevance in Data Mining

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 4904))

Abstract

Data mining and knowledge discovery is described from pattern recognition point of view along with the relevance of soft computing. The concept of computational theory of perceptions (CTP), its characteristics and the relation with fuzzy-granulation (f-granulation) are explained. Role of f-granulation in machine and human intelligence, and its modeling through rough-fuzzy integration are discussed. Three examples of synergistic integration, e.g., rough-fuzzy case generation, rough-fuzzy c-means and rough-fuzzy c-medoids are explained with their merits and role of fuzzy granular computation. Superiority, in terms of performance and computation time, is illustrated for the tasks of case generation (mining) in large scale case based reasoning systems, segmenting brain MR images, and analyzing protein sequences.

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Author information

Authors and Affiliations

  1. Indian Statistical Institute, Kolkata,  

    Sankar K. Pal

Authors
  1. Sankar K. Pal
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Editor information

Shrisha Rao Mainak Chatterjee Prasad Jayanti C. Siva Ram Murthy Sanjoy Kumar Saha

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© 2007 Springer-Verlag Berlin Heidelberg

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Pal, S.K. (2007). Rough-Fuzzy Knowledge Encoding and Uncertainty Analysis: Relevance in Data Mining. In: Rao, S., Chatterjee, M., Jayanti, P., Murthy, C.S.R., Saha, S.K. (eds) Distributed Computing and Networking. ICDCN 2008. Lecture Notes in Computer Science, vol 4904. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-77444-0_1

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  • DOI: https://doi.org/10.1007/978-3-540-77444-0_1

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-77443-3

  • Online ISBN: 978-3-540-77444-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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