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

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...

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Bibliographic Details
Published in:International journal of computer mathematics Vol. 94; no. 4; pp. 663 - 675
Main Authors: Zhang, Tengfei, Ma, Fumin
Format: Journal Article
Language:English
Published: Abingdon Taylor & Francis 03.04.2017
Taylor & Francis Ltd
Subjects:
Algorithms
Approximation
Cluster analysis
Clustering
Clustering algorithm
Clusters
Computer simulation
Gaussian function
Mathematical analysis
Mathematical models
rough k-means
rough set theory
Routing
Vector quantization
weighted distance measure
ISSN:0020-7160, 1029-0265
Online Access:Get full text
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Summary: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.
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ISSN:0020-7160
1029-0265
DOI:10.1080/00207160.2015.1124099