A robust automatic clustering algorithm for probability density functions with application to categorizing color images

ABSTRACT

This study develops a robust automatic algorithm for clustering probability density functions based on the previous research. Unlike other existing methods that often pre-determine the number of clusters, this method can self-organize data groups based on the original data structure. The proposed clustering method is also robust in regards to noise. Three examples of synthetic data and a real-world COREL dataset are utilized to illustrate the accurateness and effectiveness of the proposed approach.

KEYWORDS:

• Clustering algorithms
• COREL image database
• Kernel density method
• Probability density function

MATHEMATICS SUBJECT CLASSIFICATION:

• 62H30
• 6207

Acknowledgments

The authors would like to thank the anonymous reviewers for their constructive comments that helped improve the presentation of the article. This work is supported in part by the Ministry of Science and Technology, R.O.C., under Wen-Liang Hung's Grant: MOST 104-2118-M-134-001.

Notes

1 We herein use the terms partition and clustering of a set interchangeably.