Derivation of Linguistic Summaries is Inherently Difficult: Can Association Rule Mining Help?

Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 285)

Abstract

We present first the essence of fuzzy linguistic summaries, indicate their relation to fuzzy queries with linguistic quantifiers, and show a taxonomy of protoforms of linguistic summaries indicating that a general protoform, which corresponds to some type of an IF-THEN rule, parallels the structure and form of an association rule. We show that the use of our fuzzy querying interface makes it possible to operationalize the process of definition, updating and processing of fuzzy terms in linguistic data summaries (fuzzy values, fuzzy relations, fuzzy linguistic quantifiers, etc.) and their corresponding fuzzy association rules of a special type. We develop for them a mining algorithm based on AprioriTID. This is clearly a step towards an effective and efficient method for the generation of linguistic data summaries which is badly needed for their proliferation in practice.

Keywords

Association Rule Linguistic Term Frequent Itemsets Association Rule Mining Truth Degree 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag GmbH Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Systems Research InstitutePolish Academy of SciencesWarsawPoland
  2. 2.Technical University of RadomRadomPoland

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