Principles and practice of information theory
Principles and practice of information theory
Fuzzy clustering of elliptic ring-shaped clusters
Pattern Recognition Letters
Norm-induced shell-prototypes (NISP) clustering
Neural, Parallel & Scientific Computations
Validating fuzzy partitions obtained through c-shells clustering
Pattern Recognition Letters - Special issue on fuzzy set technology in pattern recognition
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Detection and Separation of Ring-Shaped Clusters Using Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Robust Information Clustering Algorithm
Neural Computation
Adaptive spatial information-theoretic clustering for image segmentation
Pattern Recognition
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
A contribution to convergence theory of fuzzy c-means and derivatives
IEEE Transactions on Fuzzy Systems
A possibilistic approach to clustering
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
Computation of channel capacity and rate-distortion functions
IEEE Transactions on Information Theory
Adaptive fuzzy c-shells clustering and detection of ellipses
IEEE Transactions on Neural Networks
The fuzzy c spherical shells algorithm: A new approach
IEEE Transactions on Neural Networks
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In this paper, we shall investigate source compression coding theorem from the perspective of robust fuzzy clustering that is derived from the basic fuzzy C-spherical shells (FCSS) algorithm. The proposed information fuzzy C-spherical shells (IFCSS) algorithm tackles the intertwined robust fuzzy clustering problems of outlier detection, prototype initialization and cluster validity in a unified framework of information clustering. The IFCSS addresses fuzzy membership and typicality issues separately through the minimum number and the sensitivity of hyper-parameters in the clustering objective function. We use the basic FCSS algorithm for the clustering phase to minimize the number of hyper-parameters and reduce the difficulty of prototype initialization, especially for spherical shells data. The robustness of IFCSS against noisy points (outliers) is obtained by the maximizing the mutual information (MI), which also provides a good criterion for prototype initialization. The clustering validity criterion for the IFCSS is proposed based on the structural risk minimization principle to achieve an optimal trade-off between the empirical risk (clustering) and model complexity control (cluster number). The effectiveness of the proposed algorithms for clustering spherical shells is supported by experimental results.