Interpreting and Extending Classical Agglomerative Clustering Algorithms Using a Model-Based Approach (2002)
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| Venue: | In Proceedings of 19th International Conference on Machine Learning (ICML-2002 |
| Citations: | 27 - 0 self |
BibTeX
@INPROCEEDINGS{Kamvar02interpretingand,
author = {Sepandar Kamvar and Dan Klein and Christopher Manning},
title = {Interpreting and Extending Classical Agglomerative Clustering Algorithms Using a Model-Based Approach},
booktitle = {In Proceedings of 19th International Conference on Machine Learning (ICML-2002},
year = {2002},
pages = {283--290}
}
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Abstract
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 -- Ward's method, single-link, complete-link, and a variant of group-average -- 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 viewpoint can suggest variations on these basic agglomerative algorithms.
