Algorithms for clustering data
Algorithms for clustering data
Decision estimation and classification: an introduction to pattern recognition and related topics
Decision estimation and classification: an introduction to pattern recognition and related topics
Unsupervised Optimal Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Clustering with Applications in Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Validity Measure for Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
A Distribution-Based Clustering Algorithm for Mining in Large Spatial Databases
ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
Robust clustering methods: a unified view
IEEE Transactions on Fuzzy Systems
Fuzzy shell clustering algorithms in image processing: fuzzy C-rectangular and 2-rectangular shells
IEEE Transactions on Fuzzy Systems
Fuzzy clustering for symbolic data
IEEE Transactions on Fuzzy Systems
Gaussian mixture density modeling, decomposition, and applications
IEEE Transactions on Image Processing
An improved fuzzy neural network based on T-S model
Expert Systems with Applications: An International Journal
Cybernetics and Systems Analysis
Mining typical patterns from databases
Information Sciences: an International Journal
New linguistic hedges in construction of interval type-2 FLS
ICAISC'10 Proceedings of the 10th international conference on Artifical intelligence and soft computing: Part II
Algorithm for generating fuzzy rules for WWW document classification
ICAISC'06 Proceedings of the 8th international conference on Artificial Intelligence and Soft Computing
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A new unsupervised fuzzy clustering algorithm is provided in this paper to cluster the data patterns without a priori information about the number of clusters. The initial guesses of the locations of the cluster centers or the initial guesses of the membership values are not necessary. With the minimization of a new objective function, cluster centers are generated one by one. Related centers are defined to belong to the same cluster. Multi-centers are adopted to represent the non-spherical shape of clusters. Thus, the clustering algorithm with multi-center clusters can handle non-traditional curved clusters. The proposed algorithm is tested on different data sets with a variety of cluster shapes, cluster densities, and number of points in each cluster. Also, the results are compared with some other clustering algorithms to show the effectiveness of the algorithm. Moreover, the designed unsupervised fuzzy clustering algorithm is applied to cluster the pixels in a color image to show the efficiency of the algorithm.