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Proceedings Paper

Mining fuzzy conceptual clusters and constructing the fuzzy conceptual frame lattices
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Paper Abstract

The key idea here is to use formal concept analysis and fuzzy membership criterion to partition the data space into clusters and provide knowledge through fuzzy lattices. The procedures, written here, are regarded as mapping or transform of the original space (samples) onto concepts. The mapping is further given the fuzzy membership criteria for clustering from which the clustered concepts of various degrees are found. Bucket hashing measure has been used as a measure of similarity in the proposed algorithm. The concepts are evaluated on the basis of this criterion and then they are clustered. The intuitive appeal of this approach lies in the fact that once the concepts are clustered, the data analyst is equipped with the concept measure as well as the identification of the bridging points. An interactive concept map visualization technique called Fuzzy Conceptual Frame Lattice or Fuzzy Concept Lattices is presented for user-guided knowledge discovery from the knowledge base.

Paper Details

Date Published: 12 April 2004
PDF: 8 pages
Proc. SPIE 5433, Data Mining and Knowledge Discovery: Theory, Tools, and Technology VI, (12 April 2004); doi: 10.1117/12.541790
Show Author Affiliations
Vibhu Narang, Univ. of Delhi (India)
Naveen Kumar, Univ. of Delhi (India)


Published in SPIE Proceedings Vol. 5433:
Data Mining and Knowledge Discovery: Theory, Tools, and Technology VI
Belur V. Dasarathy, Editor(s)

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