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Agglomerative Hierarchical Clustering with Constraints: Theoretical and Empirical Results (2005)
| Venue: | Lecture notes in computer science |
| Citations: | 47 - 3 self |
BibTeX
@INPROCEEDINGS{Davidson05agglomerativehierarchical,
author = {Ian Davidson and S. S. Ravi},
title = {Agglomerative Hierarchical Clustering with Constraints: Theoretical and Empirical Results},
booktitle = {Lecture notes in computer science},
year = {2005},
pages = {59--70},
publisher = {Springer}
}
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Abstract
Abstract. We explore the use of instance and cluster-level constraints with agglomerative hierarchical clustering. Though previous work has illustrated the benefits of using constraints for non-hierarchical clustering, their application to hierarchical clustering is not straight-forward for two primary reasons. First, some constraint combinations make the feasibility problem (Does there exist a single feasible solution?) NP-complete. Second, some constraint combinations when used with traditional agglomerative algorithms can cause the dendrogram to stop prematurely in a dead-end solution even though there exist other feasible solutions with a significantly smaller number of clusters. When constraints lead to efficiently solvable feasibility problems and standard agglomerative algorithms do not give rise to dead-end solutions, we empirically illustrate the benefits of using constraints to improve cluster purity and average distortion. Furthermore, we introduce the new γ constraint and use it in conjunction with the triangle inequality to considerably improve the efficiency of agglomerative clustering. 1
Keyphrases
agglomerative hierarchical clustering empirical result dead-end solution constraint combination average distortion single feasible solution feasibility problem solvable feasibility problem cluster purity standard agglomerative algorithm hierarchical clustering new constraint triangle inequality agglomerative clustering primary reason cluster-level constraint feasible solution non-hierarchical clustering traditional agglomerative algorithm previous work
