Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Fuzzy clustering of time series in the frequency domain
Information Sciences: an International Journal
Fuzzy c-means improvement using relaxed constraints support vector machines
Applied Soft Computing
Fuzzy clustering of human activity patterns
Fuzzy Sets and Systems
Fuzzy C-mean based brain MRI segmentation algorithms
Artificial Intelligence Review
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In this paper an initialization method for fuzzy c-means (FCM) algorithm is proposed in order to solve the two problems of clustering performance affected by initial cluster centers and lower computation speed for FCM. Grid and density are needed to extract approximate clustering center from sample space. Then, an initialization method for fuzzy c-means algorithm is proposed by using amount of approximate clustering centers to initialize classification number, and using approximate clustering centers to initialize initial clustering centers. Experiment shows that this method can improve clustering result and shorten clustering time validly.