A Validity Measure for Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Vector quantization and signal compression
Vector quantization and signal compression
Interval Set Clustering of Web Users with Rough K-Means
Journal of Intelligent Information Systems
Some refinements of rough k-means clustering
Pattern Recognition
A fast VQ codebook generation algorithm using codeword displacement
Pattern Recognition
RFCM: A Hybrid Clustering Algorithm Using Rough and Fuzzy Sets
Fundamenta Informaticae
VQ indexes compression and information hiding using hybrid lossless index coding
Digital Signal Processing
Reduced complexity two stage vector quantization
Digital Signal Processing
A fast k-means clustering algorithm using cluster center displacement
Pattern Recognition
Performance evaluation of density-based clustering methods
Information Sciences: an International Journal
Shadowed c-means: Integrating fuzzy and rough clustering
Pattern Recognition
Knowledge discovery with clustering based on rules by states: A water treatment application
Environmental Modelling & Software
Pairwise-adaptive dissimilarity measure for document clustering
Information Sciences: an International Journal
IEEE Transactions on Pattern Analysis and Machine Intelligence
A New Convergence Proof of Fuzzy c-Means
IEEE Transactions on Fuzzy Systems
Data mining in soft computing framework: a survey
IEEE Transactions on Neural Networks
Speaker Clustering and Cluster Purification Methods for RT07 and RT09 Evaluation Meeting Data
IEEE Transactions on Audio, Speech, and Language Processing
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In this paper, we present a rough k-means clustering algorithm based on minimizing the dissimilarity, which is defined in terms of the squared Euclidean distances between data points and their closest cluster centers. This approach is referred to as generalized rough fuzzy k-means (GRFKM) algorithm. The proposed method solves the divergence problem of available approaches, where the cluster centers may not be converged to their final positions, and reduces the number of user-defined parameters. The presented method is shown to be converged experimentally. Compared to available rough k-means clustering algorithms, the proposed method provides less computing time. Unlike available approaches, the convergence of the proposed method is independent of the used threshold value. Moreover, it yields better clustering results than RFKM for the handwritten digits data set, landsat satellite data set and synthetic data set, in terms of validity indices. Compared to MRKM and RFKM, GRFKM can reduce the value of Xie-Beni index using the handwritten digits data set, where a lower Xie-Beni index value implies the better clustering quality. The proposed method can be applied to handle real life situations needing reasoning with uncertainty.