Hierarchical clustering that takes advantage of both density-peak and density-connectivity
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2018
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| _version_ | 1866929225357852672 |
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| author | Zhu, Ye Ting, Kai Ming Jin, Yuan Angelova, Maia |
| author_facet | Zhu, Ye Ting, Kai Ming Jin, Yuan Angelova, Maia |
| contents | This paper focuses on density-based clustering, particularly the Density Peak (DP) algorithm and the one based on density-connectivity DBSCAN; and proposes a new method which takes advantage of the individual strengths of these two methods to yield a density-based hierarchical clustering algorithm. Our investigation begins with formally defining the types of clusters DP and DBSCAN are designed to detect; and then identifies the kinds of distributions that DP and DBSCAN individually fail to detect all clusters in a dataset. These identified weaknesses inspire us to formally define a new kind of clusters and propose a new method called DC-HDP to overcome these weaknesses to identify clusters with arbitrary shapes and varied densities. In addition, the new method produces a richer clustering result in terms of hierarchy or dendrogram for better cluster structures understanding. Our empirical evaluation results show that DC-HDP produces the best clustering results on 14 datasets in comparison with 7 state-of-the-art clustering algorithms. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_1810_03393 |
| institution | arXiv |
| publishDate | 2018 |
| record_format | arxiv |
| spellingShingle | Hierarchical clustering that takes advantage of both density-peak and density-connectivity Zhu, Ye Ting, Kai Ming Jin, Yuan Angelova, Maia Machine Learning Artificial Intelligence This paper focuses on density-based clustering, particularly the Density Peak (DP) algorithm and the one based on density-connectivity DBSCAN; and proposes a new method which takes advantage of the individual strengths of these two methods to yield a density-based hierarchical clustering algorithm. Our investigation begins with formally defining the types of clusters DP and DBSCAN are designed to detect; and then identifies the kinds of distributions that DP and DBSCAN individually fail to detect all clusters in a dataset. These identified weaknesses inspire us to formally define a new kind of clusters and propose a new method called DC-HDP to overcome these weaknesses to identify clusters with arbitrary shapes and varied densities. In addition, the new method produces a richer clustering result in terms of hierarchy or dendrogram for better cluster structures understanding. Our empirical evaluation results show that DC-HDP produces the best clustering results on 14 datasets in comparison with 7 state-of-the-art clustering algorithms. |
| title | Hierarchical clustering that takes advantage of both density-peak and density-connectivity |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/1810.03393 |