Algorithms for clustering data
Algorithms for clustering data
Machine Learning
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Support vector domain description
Pattern Recognition Letters - Special issue on pattern recognition in practice VI
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
A Least Biased Fuzzy Clustering Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
Optimal Cluster Preserving Embedding of Nonmetric Proximity Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Information Theory, Inference & Learning Algorithms
Information Theory, Inference & Learning Algorithms
Feature Discovery in Non-Metric Pairwise Data
The Journal of Machine Learning Research
On relational possibilistic clustering
Pattern Recognition
On the information and representation of non-Euclidean pairwise data
Pattern Recognition
A survey of kernel and spectral methods for clustering
Pattern Recognition
The possibilistic C-means algorithm: insights and recommendations
IEEE Transactions on Fuzzy Systems
Low-complexity fuzzy relational clustering algorithms for Web mining
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
Least squares quantization in PCM
IEEE Transactions on Information Theory
An introduction to kernel-based learning algorithms
IEEE Transactions on Neural Networks
Mercer kernel-based clustering in feature space
IEEE Transactions on Neural Networks
Clustering in the membership embedding space
International Journal of Knowledge Engineering and Soft Data Paradigms
Applying the possibilistic c-means algorithm in kernel-induced spaces
IEEE Transactions on Fuzzy Systems - Special section on computing with words
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Clustering is the problem of grouping objects on the basis of a similarity measure among them. Relational clustering methods can be employed when a feature-based representation of the objects is not available, and their description is given in terms of pairwise (dis)similarities. This paper focuses on the relational duals of fuzzy central clustering algorithms, and their application in situations when patterns are represented by means of non-metric pairwise dissimilarities. Symmetrization and shift operations have been proposed to transform the dissimilarities among patterns from non-metric to metric. In this paper, we analyze how four popular fuzzy central clustering algorithms are affected by such transformations. The main contributions include the lack of invariance to shift operations, as well as the invariance to symmetrization. Moreover, we highlight the connections between relational duals of central clustering algorithms and central clustering algorithms in kernel-induced spaces. One among the presented algorithms has never been proposed for non-metric relational clustering, and turns out to be very robust to shift operations.