Improving fuzzy C-means clustering algorithm based on a density-induced distance measure

The authors report an improved fuzzy C-means algorithm in comparison with the conventional one by employing a density-induced distance metric based on a novel calculation method of relative density degree. By using various synthetic and real data sets, the clustering performance of the proposed meth...

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Bibliographic Details
Published in:Journal of engineering (Stevenage, England) England), 2014-04, Vol.2014 (4), p.137-139
Main Authors: Lu, Chunhong, Xiao, Shaoqing, Gu, Xiaofeng
Format: Article
Language:English
Subjects:
density-induced distance measure
fuzzy C-means clustering algorithm
fuzzy set theory
pattern clustering
real data sets
relative density degree
synthetic data sets
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Summary:The authors report an improved fuzzy C-means algorithm in comparison with the conventional one by employing a density-induced distance metric based on a novel calculation method of relative density degree. By using various synthetic and real data sets, the clustering performance of the proposed method is systematically studied and compared with that of the conventional one. The obtained results support the conclusion that this novel method does not only inherit good characteristics of the traditional one, but also possesses improved partitions.
ISSN:2051-3305
2051-3305
DOI:10.1049/joe.2014.0053