In this paper we show that diversity-driven widening, the parallel exploration of the model space with focus on developing diverse models, can improve hierarchical agglomerative clustering. Depending on the selected linkage method, the model that is found through the widened search achieves a better silhouette coefficient than its sequentially built counterpart.
CITATION STYLE
Fillbrunn, A., & Berthold, M. R. (2015). Diversity-driven widening of hierarchical agglomerative clustering. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9385, pp. 84–94). Springer Verlag. https://doi.org/10.1007/978-3-319-24465-5_8
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