Abstract

Nonparametric clustering algorithms, including mode-seeking, valley-seeking, and unimodal set algorithms, are capable of identifying generally shaped clusters of points in metric spaces. Most mode and valley-seeking algorithms, however, are iterative and the clusters obtained are dependent on the starting classification and the assumed number of clusters. In this paper, we present a noniterative, graph-theoretic approach to nonparametric cluster analysis. The resulting algorithm is governed by a single-scalar parameter, requires no starting classification, and is capable of determining the number of clusters. The resulting clusters are unimodal sets.

Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call