Search engines fail to make a clear distinction between items of varying relevance when presenting search results to users. Instead, they rely on the user of the system to estimate which items are relevant, partially relevant, or not relevant. The user of the system is given the tedious task of distinguishing between documents that are relevant to different degrees. This often hinders the accessibility of relevant or partially relevant documents, particularly when the results set is large and many non-relevant documents are scattered throughout the set. In this paper, we present the results of a clustering scheme that groups documents within relevant, partially relevant, and not relevant clusters for a given search. A ranking algorithm accomplishes the task of clustering the documents based on system relevance. Data was collected from end-users issuing categorical, interval, and descriptive relevance judgments. The degree of overlap between users and the system for each of the clustered regions was measured. This research showed that clustering documents on the Web by regions of relevance is quite feasible.
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