Web Search Result Clustering based on Cuckoo Search and Consensus Clustering

Conventional search engines’ results are often plagued by problems like synonymy, polysemy, high volume etc. Clustering of search result other thanresolving these problems,lets user to quickly locate her information. In this paper, a method,called WSRDC-CSCC, is introduced to cluster web search result using cuckoo search meta-heuristic method and Consensus clustering. Cuckoo search provides a solid foundation for consensus clustering. As a local clustering function, k-means technique is used. The final number of cluster is not depended on this k. Consensus clustering finds the natural grouping of the objects. The proposed algorithm is compared to another clustering method which is based on cuckoo search and Bayesian Information Criterion. The experimental results show that proposed algorithm finds the actual number of clusters with great value of precision, recall and F-measure as compared to the other method.