Estimating the Optimal Number of Clusters k in a Dataset Using Data Depth

Data Science and Engineering

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[1] grid.444416.7, Department of Computer Science, Karnatak University, 580003, Dharwad, Karnataka, India;

关键词: Data depth;    Depth within cluster;    Depth between cluster;    Depth difference;    Average depth;    k;   

DOI  :  10.1007/s41019-019-0091-y

来源: publisher

【 摘 要 】

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.

【 授权许可】

CC BY