Criterion Functions for Document Clustering ∗ Experiments and Analysis

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Multi-Agent Cooperation and the Emergence of (Natural) Language (2016, background): “Purity (Zhao & Karypis, 2003) is a standard measure of cluster “quality”.”
Empirical and Theoretical Comparisons of Selected Criterion Functions for Document Clustering (2004, methodology): “The original entropy values for all the experiments presented in this paper can be found in Zhao and Karypis (2001) .”
Exploiting Wikipedia as external knowledge for document clustering (2009, background): “3 Evaluation Metrics Cluster quality is evaluated by three metrics, purity [14], F-score [10], and normalized mutual information (NMI) [15].”
Nouns are Vectors, Adjectives are Matrices: Representing Adjective-Noun Constructions in Semantic Space (2010, background): “Cluster quality is evaluated by percentage purity (Zhao and Karypis, 2003).”
Extracting semantic representations from word co-occurrence statistics: stop-lists, stemming, and SVD (2012, methodology): “The clustering and purity computations were performed on the semantic vectors using the CLUTO Clustering Toolkit (Karypis, 2003), with the direct k -way clustering algorithm and default settings and the standard purity measure (Y. Zhao & Karypis, 2001).”
Multi-view clustering (2004, background): “For a detailed description of the data sets see [19].”
A Survey of Clustering Data Mining Techniques (2006, background): “Different modifications of this objective function are evaluated in [229].”