This study proposes and evaluates a document analysis strategy for information
retrieval with visualization interfaces. The goal of document analysis is to highlight
structure that helps searchers make their own relevance judgments, rather than to shift
judgments from humans onto machines. Searchers can investigate that structure with
tools for visualizing multidimensional data.
The structure of interest in this study is discrimination of documents into clusters.
Two diagnostic measures may inform selection of document attributes for cluster
discrimination: term discrimination value and the sum of pairwise term-vector correlations. A series of experiments tests the reliability of these measures for predicting clustering tendency, as measured by proportion of elongated triples and skewness of the distribution of document dissimilarities.
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