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Estimating the Optimal Number of Clusters k in a Dataset Using Data Depth
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Estimating the Optimal Number of Clusters k in a Dataset Using Data Depth
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Journal Article
Estimating the Optimal Number of Clusters k in a Dataset Using Data Depth
2019
Overview
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, and depth difference to finalize the optimal value of
k
, which is an input value for the clustering algorithm. The experimental comparison with the leading state-of-the-art alternatives demonstrates that the proposed DeD method outperforms.
Publisher
Springer Berlin Heidelberg,Springer Nature B.V,SpringerOpen
