Abstract
Document collections are frequently encountered without labels. Labels may be determined by clustering the documents into disparate groups and implicitly finding common themes among the document clusters. This chapter describes methods for clustering documents. A key theme for document clustering is computing measures of similarity. We review the major clustering methods: k-means clustering, hierarchical clustering and the EM algorithm. Strategies for assigning meaning to algorithmically generated clusters and labels are considered. Performance evaluation helps determine the empirical characteristics of desirable clusters.
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Weiss, S.M., Indurkhya, N., Zhang, T. (2010). Finding Structure in a Document Collection. In: Fundamentals of Predictive Text Mining. Texts in Computer Science. Springer, London. https://doi.org/10.1007/978-1-84996-226-1_5
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DOI: https://doi.org/10.1007/978-1-84996-226-1_5
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