Efficient Term Frequency and Optimal Similarity Measure of Snippet for Web Search Results

All clustering methods have to assume some cluster relationship among the data objects that they are applied on. Similarity between a pair of objects can be defined either explicitly or implicitly. In this paper, we introduce a novel multi-viewpoint based similarity measure and two related clustering methods. The major difference between a traditional similarity measure and ours is that the former uses only a multi-viewpoint on clustered, which is the origin, while the latter utilizes many different viewpoints, which are objects, assumed to not be in the same cluster with the two objects being measured. Using multiple viewpoints, more informative assessment of similarity could be achieved. It combines the neighbourhood preservation capability of multidimensional content with the familiar optimal snippet-based representation by employing a multidimensional content to derive two-dimensional layouts of the query search results that preserve text similarity relations, or neighbour hoods. Theoretical analysis and empirical study are conducted to support this claim. Two criterion functions for document clustering are proposed based on this new measure. We compare them with several well-known clustering algorithms that use other popular similarity measures on various document collections to verify the advantages of our proposal.