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Data mining techniques and nature-inspired algorithms for query expansion
Ilyes Khennak (University of Sciences and Technology Houari Boumediene), Habiba Drias (University of Sciences and Technology Houari Boumediene)
Résumé : Data mining techniques and nature-inspired algorithms are currently among the most frequently used soft computing techniques for knowledge discovery, optimization, and computational intelligence. In this paper, we propose the application of both data mining techniques and nature-inspired algorithms to overcome the problem of generating the optimum expanded query in web information retrieval. We first use data mining techniques to group similar expansion term candidates into clusters. Next, we use nature-inspired algorithms to extract expansion term candidates from clusters and generate the suitable expanded query. We empirically assess the proposed approach using MEDLINE, the large online medical repository. Numerical experiments show that the proposed approach attains higher effectiveness and efficiency compared to conventional and recently published methods.