Interpreting and Extending Classical Agglomerative Clustering Algorithms using a Model-Based approach
نویسندگان
چکیده
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. We introduce adjusted complete-link, Mahalanobis-link, and line-link as variants, and demonstrate their utility.
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