Clustering web search results using conceptual grouping

Clustering Web search results facilitates users´ quick browsing through the information returned and locating interested results. This paper introduces a semantic, online clustering algorithm to automatically organize Web search results into groups. The semantic relationships among index terms are mined via the conceptual grouping and these terms are grouped to form candidate clusters related to the query topic by their semantic coherence. Then the documents are assigned to relevant clusters. The cluster labels are selected according to the importance of the terms in the search results and the clusters. Experimental results show that the proposed algorithm performs better than k-means.