Empirical and Theoretical Comparisons of Selected Criterion Functions for Document Clustering

By Ying Zhao, George Karypis. Machine-mediated learning, 2004

691 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Signal Processing

View Paper: https://www.semanticscholar.org/paper/d3321a538d8b099250a4b6f0c68e232da0724992

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Citations in the corpus

Text Mining Infrastructure in R (2008, background): “Especially in the context of clustering, specific distance measures (Zhao and Karypis 2004; Strehl et al. 2000), like the Cosine, play an important role.”
Community Structure in Large Networks: Natural Cluster Sizes and the Absence of Large Well-Defined Clusters (2008, background): “Moreover, since there exist a rich suite of both theoretical and practical algorithms [87, 149, 107, 108, 17, 95, 96, 162, 54], we can for point (4) compare and contrast several methods to approximately optimize it.”
The structure and infrastructure of the global nanotechnology literature (2006, methodology): “The clustering approach presented in this section is based on a partitional clustering algorithm [Karypis, 2005; Zhao and Karypis, 2004] contained within a software package named CLUTO.”
A Survey of Text Clustering Algorithms (2012, background): “An experimental comparison of different clustering algorithms may be found in [90] [111].”
Orthogonal nonnegative matrix t-factorizations for clustering (2006, methodology): “Entropy measures how classes distributed on various cluste rs [36].”
Social big data: Recent achievements and new challenges (2015, methodology): “In Zhao and Karypis [70], a comparative assessment of different clustering algorithms (partitioning and hierarchical) was performed using different similarity measures on high-dimensional text data.”
Hierarchical Clustering Algorithms for Document Datasets (2005, methodology): “In this paper we use six different clustering criterion functions that are defined in Table 1 and were recently compared and analyzed in a study presented in [45].”
Document clustering using particle swarm optimization (2005, background): “The data objects within each group should exhibit a large degree of similarity while the similarity among different clusters should be minimized [3, 9, 23, 25].”
Examining boredom : Different causes for different coping profiles (2015, neutral): “Although BIC was lower and the Entropy was higher in the four-class solution than the three-class solution, a closer examination of the additional class in the four-class solution revealed that it could be subsumed under one of the other three classes.”
Statistical properties of community structure in large social and information networks (2008, positive): “Since there exist a rich suite of both theoretical and practical algorithms to optimize this quantity [32, 20, 4, 17, 37, 10], we can for point (4) compare and contrast several methods to approximately optimize it.”
MOA: Massive Online Analysis, a Framework for Stream Classification and Clustering (2010, neutral): “Gamma [5] Rand statistic [35] C Index [24] Jaccard coefficient [19] Point-Biserial [30] Folkes and Mallow Index [19] Log Likelihood [22] Hubert Γ statistics [23] Dunn's Index [17] Minkowski score [12] Tau [37] Purity [44] Tau A [24] van Dongen criterion [16] Tau C [24] V-measure [38] Somer's Gamma [”
Unsupervised Learning for Expressive Speech Synthesis (2017, methodology): “An objective evaluation was performed: a small part of the corpus was labeled with expressions and character (speaker) labes (only for the evaluation purposes) and with the aid of these labels, the perplexity of the clusters was calculated, derived from entropy as by [33, 42]: PP = 2.”
Clustering on the Unit Hypersphere using von Mises-Fisher Distributions (2005, background): “Over the past few years several new approaches, ranging from spectral partitioning (Kannan et al., 2000; Zhao and Karypis, 2004), to the use of generative models from the exponential family, e.g., mixture of multinomials or Bernoulli distributions (Nigam et al., 2000) etc., have emerged.”
On the equivalence between Non-negative Matrix Factorization and Probabilistic Latent Semantic Indexing (2008, background): “More details on the purity and entropy measures can be found in Zhao and Karypis (2004).”
A new sentence similarity measure and sentence based extractive technique for automatic text summarization (2009, methodology): “Many criterion functions have been proposed in the literature (Grabmeier & Rudolph, 2002; Han & Kamber, 2006; Jain et al., 1999; Omran et al., 2007; Zhao & Karypis, 2004 ) to produce more balanced partitions.”