Fuzzy query processing using clustering techniques

Abstract

This paper addresses the problem of processsing fuzzy queries in databases and information retrieval systems. Most of the existing approaches for handling fuzziness in queries require explicit definitions of fuzziness and membership functions. We propose an architecture and data structures for a fuzzy query processor that utilizes clustering techniques as a tool to generate the mapping between fuzzy terms, defined at a high level of abstraction and the data items of the database records. The clustering techniques developed in this paper are based on multiple thresholding of fuzzy clustering. The use of thresholded fuzzy clustering provides a controlled overlap between clusters of records and thus reflects, naturally, the required fuzziness in the response. A prototype fuzzy query processor based on this approach has been implemented and tested on a sample database.