An Evaluation of Techniques for Clustering Search Results
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.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jan 01, 2005
- Accession Number
- ADA439790
Entities
People
- Anton V. Leouski
- W. Bruce Croft
Organizations
- University of Massachusetts Amherst