Convergence theory for fuzzy c-means: counterexamples and repairs
IEEE Transactions on Systems, Man and Cybernetics
Neural Networks
Automatic subspace clustering of high dimensional data for data mining applications
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
OPTICS: ordering points to identify the clustering structure
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
FREM: fast and robust EM clustering for large data sets
Proceedings of the eleventh international conference on Information and knowledge management
BIRCH: A New Data Clustering Algorithm and Its Applications
Data Mining and Knowledge Discovery
WaveCluster: A Multi-Resolution Clustering Approach for Very Large Spatial Databases
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Efficient and Effective Clustering Methods for Spatial Data Mining
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
STING: A Statistical Information Grid Approach to Spatial Data Mining
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
Suppressed fuzzy c-means clustering algorithm
Pattern Recognition Letters
Cluster ensembles --- a knowledge reuse framework for combining multiple partitions
The Journal of Machine Learning Research
Simple Gabor feature space for invariant object recognition
Pattern Recognition Letters
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
Robust maximum entropy clustering algorithm with its labeling for outliers
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Possibility Theoretic Clustering and its Preliminary Application to Large Image Segmentation
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Attribute weighted mercer kernel based fuzzy clustering algorithm for general non-spherical datasets
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Cluster analysis of gene expression data based on self-splitting and merging competitive learning
IEEE Transactions on Information Technology in Biomedicine
Alpha-Cut Implemented Fuzzy Clustering Algorithms and Switching Regressions
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Fuzzy shell clustering algorithms in image processing: fuzzy C-rectangular and 2-rectangular shells
IEEE Transactions on Fuzzy Systems
A contribution to convergence theory of fuzzy c-means and derivatives
IEEE Transactions on Fuzzy Systems
Uncertain Fuzzy Clustering: Interval Type-2 Fuzzy Approach to C-Means
IEEE Transactions on Fuzzy Systems
Self-splitting competitive learning: a new on-line clustering paradigm
IEEE Transactions on Neural Networks
A framework with modified fast FCM for brain MR images segmentation
Pattern Recognition
Partitive clustering (K-means family)
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
Fuzzy c-means clustering with bilateral filtering for medical image segmentation
HAIS'12 Proceedings of the 7th international conference on Hybrid Artificial Intelligent Systems - Volume Part I
Dynamic rough clustering and its applications
Applied Soft Computing
Kernel generalized fuzzy c-means clustering with spatial information for image segmentation
Digital Signal Processing
Evolving soft subspace clustering
Applied Soft Computing
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The fuzziness index m has important influence on the clustering result of fuzzy clustering algorithms, and it should not be forced to fix at the usual value m = 2. In view of its distinctive features in applications and its limitation in having m = 2 only, a recent advance of fuzzy clustering called fuzzy c-means clustering with improved fuzzy partitions (IFP-FCM) is extended in this paper, and a generalized algorithm called GIFP-FCM for more effective clustering is proposed. By introducing a novel membership constraint function, a new objective function is constructed, and furthermore, GIFP-FCM clustering is derived. Meanwhile, from the viewpoints of Lp norm distance measure and competitive learning, the robustness and convergence of the proposed algorithm are analyzed. Furthermore, the classical fuzzy c-means algorithm (FCM) and IFP-FCM can be taken as two special cases of the proposed algorithm. Several experimental results including its application to noisy image texture segmentation are presented to demonstrate its average advantage over FCM and IFP-FCM in both clustering and robustness capabilities.