Diversity-Driven Widening of Hierarchical Agglomerative Clustering
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...
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Published in | Advances in Intelligent Data Analysis XIV Vol. 9385; pp. 84 - 94 |
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Main Authors | , |
Format | Book Chapter |
Language | English |
Published |
Switzerland
Springer International Publishing AG
2015
Springer International Publishing |
Series | Lecture Notes in Computer Science |
Subjects | |
Online Access | Get full text |
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Abstract | 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. |
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AbstractList | 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. |
Author | Fillbrunn, Alexander Berthold, Michael R. |
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Snippet | In this paper we show that diversity-driven widening, the parallel exploration of the model space with focus on developing diverse models, can improve... |
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SubjectTerms | Agglomerative Hierarchical Clustering Algorithm Artificial intelligence Davies-Bouldin Index (DBI) Image processing Initial Distance Matrix Pattern recognition Seeds Dataset Silhouette Coefficient |
Title | Diversity-Driven Widening of Hierarchical Agglomerative Clustering |
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