Gaussian clustering method based on maximum-fuzzy-entropy interpretation
Fuzzy Sets and Systems
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
ACM Computing Surveys (CSUR)
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fuzzy and Neural Approaches in Engineering
Fuzzy and Neural Approaches in Engineering
Mean Shift, Mode Seeking, and Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Algorithm for Data-Driven Bandwidth Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
A survey of fuzzy clustering algorithms for pattern recognition. II
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Model transitions in descending FLVQ
IEEE Transactions on Neural Networks
Molecular dynamics-like data clustering approach
Pattern Recognition
Expert Systems with Applications: An International Journal
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Data clustering is an important part of cluster analysis. Numerous clustering algorithms based on various theories have been developed, and new algorithms continue to appear in the literature. In this paper, supposing that each cluster center is a gravity center and each data point has a constant mass, Newton's law of gravity is transformed from m/d2to 1/d2. According to adapted the law, we have proposed novel method called Gravitational Fuzzy clustering. The three main contributions of new algorithm can be summarized as: 1) it becomes more sophisticated technique by taking advantages of K-means, fuzzy C-means and subtractive clustering methods, 2) it removes the dependence on initial condition by taking account of the gravitation effect, 3) it improves the cluster centers by means of the gravity center of clusters. We illustrate the advantage of the resulting of gravitational approach with several examples.