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

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,...

Full description

Bibliographic Details
Main Authors: Channamma Patil, Ishwar Baidari
Format: Article
Language:English
Published: SpringerOpen 2019-06-01
Series:Data Science and Engineering
Subjects:
Data depth
Depth within cluster
Depth between cluster
Depth difference
Average depth
Optimal value k
Online Access:http://link.springer.com/article/10.1007/s41019-019-0091-y

Internet

http://link.springer.com/article/10.1007/s41019-019-0091-y

Similar Items