Improved rough k-means clustering algorithm based on weighted distance measure with Gaussian function. Issue 4 (3rd April 2017)

• Record Type: Journal Article
• Authors: Zhang, Tengfei; Ma, Fumin
• Is Part Of: International journal of computer mathematics. Volume 94:Issue 4(2017)
• Page Start: 663
• Page End: 675
• Publication Date: 2017-04-03
• Subjects: Clustering algorithm -- rough k-means -- weighted distance measure -- rough set theory -- Gaussian function; 62H30 -- 91C20 -- 68T10 -- 68T30; Computers -- Periodicals; Numerical analysis -- Periodicals; Automation -- Periodicals; 004.0151
• DOI: 10.1080/00207160.2015.1124099
• Languages: English
• ISSNs: 0020-7160

Abstract

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.