Bit Reduction Support Vector Machine
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Analytically tractable case of fuzzy c-means clustering
Pattern Recognition
Immune-based evolutionary algorithm for fabric evaluation
Mathematics and Computers in Simulation
A Gaussian kernel-based fuzzy c-means algorithm with a spatial bias correction
Pattern Recognition Letters
LSSVM with Fuzzy Pre-processing Model Based Aero Engine Data Mining Technology
ADMA '07 Proceedings of the 3rd international conference on Advanced Data Mining and Applications
A Scalable Framework For Segmenting Magnetic Resonance Images
Journal of Signal Processing Systems
On the efficiency of evolutionary fuzzy clustering
Journal of Heuristics
A scalable framework for cluster ensembles
Pattern Recognition
Extending fuzzy and probabilistic clustering to very large data sets
Computational Statistics & Data Analysis
Fast support vector machines for continuous data
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on cybernetics and cognitive informatics
Density-weighted fuzzy c-means clustering
IEEE Transactions on Fuzzy Systems
A weight-featured and data-distribution-based fuzzy pattern classification approach
Control and Intelligent Systems
Data clustering: 50 years beyond K-means
Pattern Recognition Letters
A fuzzy clustering approach using reward and penalty functions
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 6
A fuzzy logic based approach to feedback reinforcement in image retrieval
ICIC'09 Proceedings of the 5th international conference on Emerging intelligent computing technology and applications
A BCM theory of meta-plasticity for online self-reorganizing fuzzy-associative learning
IEEE Transactions on Neural Networks
A time-efficient pattern reduction algorithm for k-means clustering
Information Sciences: an International Journal
Distributed learning with data reduction
Transactions on computational collective intelligence IV
Expert Systems with Applications: An International Journal
SIC-means: a semi-fuzzy approach for clustering data streams using c-means
ANNPR'10 Proceedings of the 4th IAPR TC3 conference on Artificial Neural Networks in Pattern Recognition
Partitive clustering (K-means family)
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
Objective function-based clustering
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
Semi-supervised fuzzy clustering with metric learning and entropy regularization
Knowledge-Based Systems
Regularized soft K-means for discriminant analysis
Neurocomputing
AICI'12 Proceedings of the 4th international conference on Artificial Intelligence and Computational Intelligence
Information Sciences: an International Journal
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Clustering is a useful approach in image segmentation, data mining, and other pattern recognition problems for which unlabeled data exist. Fuzzy clustering using fuzzy c-means or variants of it can provide a data partition that is both better and more meaningful than hard clustering approaches. The clustering process can be quite slow when there are many objects or patterns to be clustered. This paper discusses the algorithm brFCM, which is able to reduce the number of distinct patterns which must be clustered without adversely affecting the partition quality. The reduction is done by aggregating similar examples and then using a weighted exemplar in the clustering process. The reduction in the amount of clustering data allows a partition of the data to be produced faster. The algorithm is applied to the problem of segmenting 32 magnetic resonance images into different tissue types and the problem of segmenting 172 infrared images into trees, grass and target. Average speed-ups of as much as 59-290 times a traditional implementation of fuzzy c-means were obtained using brFCM, while producing partitions that are equivalent to those produced by fuzzy c-means.