A novel multiseed nonhierarchical data clustering technique

Authors:
D. Chaudhuri;B. B. Chaudhuri
Affiliations:
Indian Stat. Inst., Calcutta;-
Venue:
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Year:
1997

Citing 0
Cited 4

A novel approach to fuzzy clustering based on a dissimilarity relation extracted from data using a TS system

Pattern Recognition
Single point iterative weighted fuzzy C-means clustering algorithm for remote sensing image segmentation

Pattern Recognition
Key Point Based Data Analysis Technique

ICIC '07 Proceedings of the 3rd International Conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence
Study on multi-center fuzzy C-means algorithm based on transitive closure and spectral clustering

Applied Soft Computing

Quantified Score

Hi-index	0.00

Visualization

Abstract

Clustering techniques such as K-means and Forgy as well as their improved version ISODATA group data around one seed point for each cluster, It is well known that these methods do not work well if the shape of the cluster is elongated or nonconvex. We argue that for a elongated or nonconvex shaped cluster, more than one seed is needed, In this paper a multiseed clustering algorithm is proposed. A density based representative point selection algorithm is used to choose the initial seed points. To assign several seed points to one cluster, a minimal spanning tree guided novel technique is proposed. Also, a border point detection algorithm is proposed for the detection of shape of the cluster. This border in turn signifies whether the cluster is elongated or not, Experimental results show the efficiency of this clustering technique