On a class of fuzzy classification maximum likelihood procedures
Fuzzy Sets and Systems
Pattern Recognition Letters - Special issue on fuzzy set technology in pattern recognition
ACM Computing Surveys (CSUR)
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Mean Shift, Mode Seeking, and Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Similarity-Based Robust Clustering Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
General C-Means Clustering Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition
A Novel Multimodal Probability Model for Cluster Analysis
RSKT '09 Proceedings of the 4th International Conference on Rough Sets and Knowledge Technology
Fuzzy clustering algorithms for unsupervised change detection in remote sensing images
Information Sciences: an International Journal
Data clustering by minimizing disconnectivity
Information Sciences: an International Journal
Gene transposon based clone selection algorithm for automatic clustering
Information Sciences: an International Journal
Alpha-Cut Implemented Fuzzy Clustering Algorithms and Switching Regressions
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Optimality test for generalized FCM and its application to parameter selection
IEEE Transactions on Fuzzy Systems
A Robust Automatic Merging Possibilistic Clustering Method
IEEE Transactions on Fuzzy Systems
A possibilistic approach to clustering
IEEE Transactions on Fuzzy Systems
On cluster validity for the fuzzy c-means model
IEEE Transactions on Fuzzy Systems
Mathematical and Computer Modelling: An International Journal
A robust EM clustering algorithm for Gaussian mixture models
Pattern Recognition
Efficient stochastic algorithms for document clustering
Information Sciences: an International Journal
A modification of the Lloyd algorithm for k-anonymous quantization
Information Sciences: an International Journal
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In 2009, Yu et al. proposed a multimodal probability model (MPM) for clustering. This paper makes advanced clustering constructions on the MPM. We first reconstruct most existing clustering algorithms, such as the k-means, fuzzy c-means, possibilistic c-means, mean shift, classification maximum likelihood, and latent class methods, by establishing the relationships between these clustering algorithms and the MPM. Under our clustering construction, we find that the MPM can be seen as a basic probability model for most existing clustering algorithms. We then construct new clustering frameworks based on the MPM. One of the frameworks develops new penalized-type clustering algorithms. Another one induces entropy-type clustering algorithms, especially with sample-weighted clustering. Several numerical and real data sets are made for comparisons. These experimental results show that our clustering constructions based on the MPM can produce useful and effective clustering algorithms.