Web Search Result Clustering based on Cuckoo Search and Consensus Clustering

摘要

Clustering of web search result document has emerged as a promising tool forimproving retrieval performance of an Information Retrieval (IR) system. Searchresults often plagued by problems like synonymy, polysemy, high volume etc.Clustering other than resolving these problems also provides the user theeasiness to locate his/her desired information. In this paper, a method, calledWSRDC-CSCC, is introduced to cluster web search result using cuckoo searchmeta-heuristic method and Consensus clustering. Cuckoo search provides a solidfoundation for consensus clustering. As a local clustering function, k-meanstechnique is used. The final number of cluster is not depended on this k.Consensus clustering finds the natural grouping of the objects. The proposedalgorithm is compared to another clustering method which is based on cuckoosearch and Bayesian Information Criterion. The experimental results show thatproposed algorithm finds the actual number of clusters with great value ofprecision, recall and F-measure as compared to the other method