title: Interpreting and Extending Classical Agglomerative Clustering Algorithms using a Model-Based Approach
creator: Kamvar, Sepandar D.
creator: Klein, Dan
creator: Manning, Christopher D.
subject: Computer Science
subject: Data Mining
subject: Miscellaneous
description: 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, group-average, 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.
publisher: Stanford
date: 2002-02
type: Techreport
type: NonPeerReviewed
format: application/pdf
identifier: http://ilpubs.stanford.edu:8090/529/1/2002-11.pdf
identifier: Kamvar, Sepandar D. and Klein, Dan and Manning, Christopher D. (2002) Interpreting and Extending Classical Agglomerative Clustering Algorithms using a Model-Based Approach. Technical Report. Stanford.
relation: http://ilpubs.stanford.edu:8090/529/