Clustering of web search result document has emerged as a promising tool for\nimproving retrieval performance of an Information Retrieval (IR) system. Search\nresults often plagued by problems like synonymy, polysemy, high volume etc.\nClustering other than resolving these problems also provides the user the\neasiness to locate his/her desired information. In this paper, a method, called\nWSRDC-CSCC, is introduced to cluster web search result using cuckoo search\nmeta-heuristic method and Consensus clustering. Cuckoo search provides a solid\nfoundation for consensus clustering. As a local clustering function, k-means\ntechnique is used. The final number of cluster is not depended on this k.\nConsensus clustering finds the natural grouping of the objects. The proposed\nalgorithm is compared to another clustering method which is based on cuckoo\nsearch and Bayesian Information Criterion. The experimental results show that\nproposed algorithm finds the actual number of clusters with great value of\nprecision, recall and F-measure as compared to the other method\n