Evaluation of hierarchical clustering algorithms for document datasets

By Ying Zhao, George Karypis. International Conference on Information and Knowledge Management, 2002

718 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Signal Processing

View Paper: https://www.semanticscholar.org/paper/320bdc47fc1e8682308d2008f25dbc2939d0a892

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Citations in the corpus

Understanding of Internal Clustering Validation Measures (2010, background): “As the goal of clustering is to make objects within the same cluster similar and objects in different clusters distinct, internal validation measures are often based on the following two criteria [4] [5].”
Similarity Measures for Text Document Clustering (2008, methodology): “The quality of a clustering result was evaluated using two evaluation measures—purity and entropy, which are widely used to evaluate the performance of unsupervised learning algorithms [23, 22].”
Social big data: Recent achievements and new challenges (2015, background): “hat are optimal in terms of some predefined criteria functions [68] nd hierarchical algorithms that group the data points into a hierarhical tree structure or a dendrogram [69].”
Hierarchical Clustering Algorithms for Document Datasets (2005, background): “A shorter version of this paper has previously appeared in [44].”
Ancestry informative markers for fine-scale individual assignment to worldwide populations (2010, neutral): “More speciﬁcally, we used the publicly available software ClutoCluster [35] with default parameters.”
From Frequency to Meaning: Vector Space Models of Semantics (2010, background): “The clusters may be partitional (flat) (Cutting, Karger, Pedersen, & Tukey, 1992; Pantel & Lin, 2002b) or they may have a hierarchical structure (groups of groups) (Zhao & Karypis, 2002); they may be non-overlapping (hard) (Croft, 1977) or overlapping (soft) (Zamir & Etzioni, 1999).”
Comprehensive survey on hierarchical clustering algorithms and the recent developments (2022, methodology): “…available from the same cluster, a distance threshold-based graph is first constructed from the documents dataset and the graph is partitioned to obtain the constraint clusters from which the final hierarchical solution is constructed using the UPGMA agglomerative scheme (Zhao and Karypis 2002).”