A Gravitational Fuzzy C-Means Clustering Algorithm Based on Density Weight

Tian Wu Zhang; Gong Bing Guo

doi:10.4028/www.scientific.net/AMM.182-183.1681

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Level Set Method for Topological Optimization of the Naiver-Stokes Fluid Flow
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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 182-183A Gravitational Fuzzy C-Means Clustering Algorithm...

A Gravitational Fuzzy C-Means Clustering Algorithm Based on Density Weight

Abstract:

Fuzzy C-means clustering algorithm(FCM) is sensitive to its initialization of value and noise data and easy to fall into local minimum points, while it can’t get the global optimal solution. This paper introduces gravitation and density weight into the process of clustering, and proposes a gravitational Fuzzy C-Means clustering algorithm based on density weight (DWGFCM). The experimental results show that the algorithm has better global optimal solution, overcomes the shortcomings of traditional Fuzzy C-means clustering algorithm. Clustering results are obviously better than FCM algorithm.

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Periodical:

Applied Mechanics and Materials (Volumes 182-183)

Pages:

1681-1685

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.182-183.1681

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Online since:

June 2012

Authors:

Tian Wu Zhang, Gong Bing Guo

Keywords:

Data Clustering, Density Weight, Fuzzy C-Means Clustering Algorithm, Gravitation

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