Optimised K-means for web search

摘要

With the vast amount of information available online, searching documents relevant to a given query requires the user to go through many titles and snippets. This searching time can be reduced by grouping search results into clusters so that the user can select the relevant cluster at a glance by looking at the cluster labels. A new method of search results clustering is introduced in this paper which clusters the search results using optimised K-means algorithm using the terms from URL, title tag and meta tag as features. Optimisation of K-means algorithm is done by selecting the initial centroids using scale factor method. The proposed method of clustering is compared with existing snippet clustering algorithms in terms of intra-cluster distance and inter-cluster distance. Results show that the proposed method produces high quality clusters than the existing methods.