Algorithms for clustering data
Algorithms for clustering data
A Validity Measure for Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Machine Learning
On Clustering Validation Techniques
Journal of Intelligent Information Systems
The Journal of Machine Learning Research
Fuzzy cluster validation index based on inter-cluster proximity
Pattern Recognition Letters
A Novel Kernel Method for Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
A cluster validity index for fuzzy clustering
Pattern Recognition Letters
New indices for cluster validity assessment
Pattern Recognition Letters
An objective approach to cluster validation
Pattern Recognition Letters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Some new indexes of cluster validity
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Validity-guided (re)clustering with applications to image segmentation
IEEE Transactions on Fuzzy Systems
A new kernel-based fuzzy clustering approach: support vector clustering with cell growing
IEEE Transactions on Fuzzy Systems
A new separation measure for improving the effectiveness of validity indices
Information Sciences: an International Journal
MMSVC: an efficient unsupervised learning approach for large-scale datasets
LSMS/ICSEE'10 Proceedings of the 2010 international conference on Life system modeling and simulation and intelligent computing, and 2010 international conference on Intelligent computing for sustainable energy and environment: Part III
Dampster-Shafer evidence theory based multi-characteristics fusion for clustering evaluation
RSKT'10 Proceedings of the 5th international conference on Rough set and knowledge technology
Position regularized Support Vector Domain Description
Pattern Recognition
| Hi-index | 0.01 |
This paper presents a cluster validity measure with a hybrid parameter search method for the support vector clustering (SVC) algorithm to identify an optimal cluster structure for a given data set. The cluster structure obtained by the SVC is controlled by two parameters: the parameter of kernel functions, denoted as q; and the soft-margin constant of Lagrangian functions, denoted as C. Large trial-and-error search efforts on these two parameters are necessary for reaching a satisfactory clustering result. From intensive observations of the behavior of the cluster splitting, we found that (1) the overall search range of q is related to the densities of the clusters; (2) each cluster structure corresponds to an interval of q, and the size of each interval is different; and (3) identifying the optimal structure is equivalent to finding the largest interval among all intervals. We have based our findings on developing a validity measure with an ad hoc parameter search algorithm to enable the SVC algorithm to identify optimal cluster configurations with a minimal number of executions. Computer simulations have been conducted on benchmark data sets to demonstrate the effectiveness and robustness of our proposed approach.