Web Search Result Clustering based on Cuckoo Search and Consensus Clustering

TL;DR

This paper introduces WSRDC-CSCC, a novel web search result clustering method combining cuckoo search meta-heuristic and consensus clustering, improving accuracy in identifying natural groupings.

Contribution

It presents a new clustering algorithm that integrates cuckoo search with consensus clustering, outperforming previous cuckoo search-based methods in accuracy and cluster detection.

Findings

The proposed method accurately finds the natural number of clusters.

It achieves higher precision, recall, and F-measure than existing cuckoo search-based clustering.

Experimental results demonstrate improved clustering performance.

Abstract

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