Estimating the Optimal Number of Clusters k in a Dataset Using Data Depth
Abstract This paper proposes a new method called depth difference (DeD), for estimating the optimal number of clusters (k) in a dataset based on data depth. The DeD method estimates the k parameter before actual clustering is constructed. We define the depth within clusters, depth between clusters,...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2019-06-01
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| Series: | Data Science and Engineering |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1007/s41019-019-0091-y |