Identification of piecewise affine systems by means of fuzzy clustering and competitive learning
Engineering Applications of Artificial Intelligence
A Scalable Framework For Segmenting Magnetic Resonance Images
Journal of Signal Processing Systems
On the efficiency of evolutionary fuzzy clustering
Journal of Heuristics
International Journal of Robotics Research
Extending fuzzy and probabilistic clustering to very large data sets
Computational Statistics & Data Analysis
Density-weighted fuzzy c-means clustering
IEEE Transactions on Fuzzy Systems
Clustering: A neural network approach
Neural Networks
Classifying Japanese polysemous verbs based on fuzzy C-means clustering
TextGraphs-4 Proceedings of the 2009 Workshop on Graph-based Methods for Natural Language Processing
DNA computing approach to management engineering
KES'07/WIRN'07 Proceedings of the 11th international conference, KES 2007 and XVII Italian workshop on neural networks conference on Knowledge-based intelligent information and engineering systems: Part III
Application of improved fuzzy C-means clustering in detecting human head
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 3
Improving the performance of k-means for color quantization
Image and Vision Computing
Evolutionary fuzzy clustering of relational data
Theoretical Computer Science
Partitioning hard clustering algorithms based on multiple dissimilarity matrices
Pattern Recognition
Automatic aspect discrimination in data clustering
Pattern Recognition
Relative entropy fuzzy c-means clustering
Information Sciences: an International Journal
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In this paper, we present an efficient implementation of the fuzzy c-means clustering algorithm. The original algorithm alternates between estimating centers of the clusters and the fuzzy membership of the data points. The size of the membership matrix is on the order of the original data set, a prohibitive size if this technique is to be applied to very large data sets with many clusters. Our implementation eliminates the storage of this data structure by combining the two updates into a single update of the cluster centers. This change significantly affects the asymptotic runtime as the new algorithm is linear with respect to the number of clusters, while the original is quadratic. Elimination of the membership matrix also reduces the overhead associated with repeatedly accessing a large data structure. Empirical evidence is presented to quantify the savings achieved by this new method