  Clustering of web search result document has emerged as a promising tool for
improving retrieval performance of an Information Retrieval (IR) system. Search
results often plagued by problems like synonymy, polysemy, high volume etc.
Clustering other than resolving these problems also provides the user the
easiness to locate his/her desired 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
