8 August 2007 Cloud model based fuzzy C-means clustering and its application
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The Algorithm of Fuzzy C-Means (FCM) clustering is used in many fields, such as data mining, image segmentation etc. But it has the problem of cluster center initialization. Good initial cluster centers will constrain the value function to the overall situation optimal solution rapidly, and inappropriate initial cluster centers, not only need more iterative times, but also may possibly cause the algorithm finally restrained to the partial optimal solution. Aim to resolve the problem of cluster center initialization, the paper proposes a new approach of FCM based on cloud model which is an efficient transformation model between quantitative number and qualitative concept, and applied it in the field of image segmentation, the experiment results prove the method can define good initial cluster centers and produce good quality of image segmentation.
© (2007) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Kun Qin, Kun Qin, Min Xu, Min Xu, Deyi Li, Deyi Li, } "Cloud model based fuzzy C-means clustering and its application", Proc. SPIE 6752, Geoinformatics 2007: Remotely Sensed Data and Information, 67520T (8 August 2007); doi: 10.1117/12.760463; https://doi.org/10.1117/12.760463


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