Visually exploring concept-based fuzzy clusters in web search results

Abstract

Users of web search systems often have difficulty determining the relevance of search results to their information needs. Clustering has been suggested as a method for making this task easier. However, this introduces new challenges such as naming the clusters, selecting multiple clusters, and re-sorting the search results based on the cluster information. To address these challenges, we have developed Concept Highlighter, a tool for visually exploring concept-based fuzzy clusters in web search results. This tool automatically generates a set of concepts related to the users' queries, and performs single-pass fuzzy c-means clustering on the search results using these concepts as the cluster centroids. A visual interface is provided for interactively exploring the search results. In this paper, we describe the features of Concept Highlighter and its use in finding relevant documents within the search results through concept selection and document surrogate highlighting. © Springer-Verlag Berlin Heidelberg 2006.