Heuristic algorithm for interpretation of multi-valued attributes in similarity-based fuzzy relational databases

  • Authors:
  • Rafal A. Angryk;Jacek Czerniak

  • Affiliations:
  • Department of Computer Science, Montana State University, Bozeman, MT 59717-3880, USA;Systems Research Institute, Polish Academy of Sciences, Laboratory of Intelligent Systems, 01-447 Warszawa, Poland

  • Venue:
  • International Journal of Approximate Reasoning
  • Year:
  • 2010

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Abstract

In this work, we are presenting implementation details and extended scalability tests of the heuristic algorithm, which we had used in the past [1,2] to discover knowledge from multi-valued data entries stored in similarity-based fuzzy relational databases. The multi-valued symbolic descriptors, characterizing individual attributes of database records, are commonly used in similarity-based fuzzy databases to reflect uncertainty about the recorded observation. In this paper, we present an algorithm, which we developed to precisely interpret such non-atomic values and to transfer the fuzzy database tuples to the forms acceptable for many regular (i.e. atomic values based) data mining algorithms.