Publication Date
1996
Abstract
The ability to effectively organize retrieval results becomes more important as the focus of Information Retrieval (IR) shifts towards interactive search processes. Automatic classification techniques are capable of providing the necessary information organization by arranging the retrieved data into groups of documents with common subjects. In this paper, we compare classification methods from IR and Machine Learning (ML) for clustering search results. Issues such as document representation, classification algorithms, and cluster representation are discussed. We introduce several evaluation techniques and use them in preliminary experiments. These experiments indicate that the proposed techniques have promise, but it is clear that user experiments are required to carry out more thorough evaluation.
Recommended Citation
Croft, W. Bruce and Leouski, Anton V., "An Evaluation of Techniques for Clustering Search Results" (1996). Computer Science Department Faculty Publication Series. 36.
Retrieved from https://scholarworks.umass.edu/cs_faculty_pubs/36
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