Scatter/Gather: a cluster-based approach to browsing large document collections
SIGIR '92 Proceedings of the 15th annual international ACM SIGIR conference on Research and development in information retrieval
Bringing order to the Web: automatically categorizing search results
Proceedings of the SIGCHI conference on Human Factors in Computing Systems
Learning to cluster web search results
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Using query profiles for clarification
ECIR'06 Proceedings of the 28th European conference on Advances in Information Retrieval
Identifying and ranking possible semantic and common usage categories of search engine queries
WISE'10 Proceedings of the 11th international conference on Web information systems engineering
Journal of Intelligent Information Systems
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In the context of Web Search, clustering based engines are emerging as an alternative for the classical ones. In this paper we analyse different possible ranking algorithms for ordering clusters of documents within a search result. More specifically, we investigate approaches based on document rankings, on the similarities between the user query and the search results, on the quality of the produced clusters, as well as some document independent approaches. Even though we use a topic based hierarchy for categorizing the URLs, our metrics can be applied to other clusters as well. An empirical analysis with a group of 20 subjects showed that the average similarity between the user query and the documents within each category yields the best cluster ranking.