Implementation of web search result clustering system

Abstract

Web search results clustering is an increasingly popular technique for providing useful grouping of web search results. This paper introduces a prototype web search results clustering engine that use the random sampling technique with medoids instead of centroids to improve clustering quality, Cluster labeling is achieved by combining intra-cluster and inter-cluster term extraction based on a variant of the information gain measure by using Modified Furthest Point First algorithm. M-FPF is compared against two other established web document clustering algorithms: Suffix Tree Clustering (STC) and Lingo, which are provided by the free open source Carrot2 Document Clustering Workbench. We measure cluster quality by considering precision , recall and relevance. Results from testing on different datasets show a considerable clustering quality. © 2013 Springer.