Improved rough k-means clustering algorithm based on weighted distance measure with Gaussian function

ABSTRACT

Rough k-means clustering algorithm and its extensions are introduced and successfully applied to real-life data where clusters do not necessarily have crisp boundaries. Experiments with the rough k-means clustering algorithm have shown that it provides a reasonable set of lower and upper bounds for a given dataset. However, the same weight was used for all the data objects in a lower or upper approximate set when computing the new centre for each cluster while the different impacts of the objects in a same approximation were ignored. An improved rough k-means clustering based on weighted distance measure with Gaussian function is proposed in this paper. The validity of this algorithm is demonstrated by simulation and experimental analysis.

KEYWORDS:

• Clustering algorithm
• rough k-means
• weighted distance measure
• rough set theory
• Gaussian function

2010 AMS SUBJECT CLASSIFICATIONS:

• 62H30
• 91C20
• 68T10
• 68T30

Acknowledgements

The authors would like to thank the editor and the anonymous reviewers for their valuable comments that helped to improve the paper greatly.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This research was supported by the National Natural Science Foundation of China (61105082, 61403184), Natural Science Foundation of Jiangsu Province (BK2012470), Jiangsu Government Scholarship for Overseas Studies (JS-2013-219, JS-2013-342), and ‘1311 Talent Plan’ of Nanjing University of Posts and Communications (NY2013).