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ICML
2002
IEEE

Interpreting and Extending Classical Agglomerative Clustering Algorithms using a Model-Based approach

14 years 4 months ago
Interpreting and Extending Classical Agglomerative Clustering Algorithms using a Model-Based approach
We present two results which arise from a model-based approach to hierarchical agglomerative clustering. First, we show formally that the common heuristic agglomerative clustering algorithms ? single-link, complete-link, groupaverage, and Ward's method ? are each equivalent to a hierarchical model-based method. This interpretation gives a theoretical explanation of the empirical behavior of these algorithms, as well as a principled approach to resolving practical issues, such as number of clusters or the choice of method. Second, we show how a model-based approach can be used to extend these basic agglomerative algorithms. We introduce adjusted complete-link, Mahalanobis-link, and line-link as variants of the classical agglomerative methods, and demonstrate their utility.
Sepandar D. Kamvar, Dan Klein, Christopher D. Mann
Added 17 Nov 2009
Updated 17 Nov 2009
Type Conference
Year 2002
Where ICML
Authors Sepandar D. Kamvar, Dan Klein, Christopher D. Manning
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