Estimating the Optimal Number of Clusters k in a Dataset Using Data Depth
Channamma Patil, Ishwar Baidari · Data Science and Engineering · 2019
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.