Implementation of Web Search Result Clustering System

摘要

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.