A Validity Measure for Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Least Squares Support Vector Machine Classifiers
Neural Processing Letters
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Neural Computation
On the algorithmic implementation of multiclass kernel-based vector machines
The Journal of Machine Learning Research
Convex Optimization
Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples
The Journal of Machine Learning Research
More efficiency in multiple kernel learning
Proceedings of the 24th international conference on Machine learning
Upper and lower values for the level of fuzziness in FCM
Information Sciences: an International Journal
Advances in Fuzzy Clustering and its Applications
Advances in Fuzzy Clustering and its Applications
Unsupervised and semi-supervised multi-class support vector machines
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
Maximum margin clustering made practical
IEEE Transactions on Neural Networks
Locality sensitive C-means clustering algorithms
Neurocomputing
Fuzzy clustering algorithms for unsupervised change detection in remote sensing images
Information Sciences: an International Journal
Fuzzy clustering of time series in the frequency domain
Information Sciences: an International Journal
A fuzzy minimax clustering model and its applications
Information Sciences: an International Journal
Semi-Supervised Maximum Margin Clustering with Pairwise Constraints
IEEE Transactions on Knowledge and Data Engineering
Fast accurate fuzzy clustering through data reduction
IEEE Transactions on Fuzzy Systems
On cluster validity for the fuzzy c-means model
IEEE Transactions on Fuzzy Systems
A reduced support vector machine approach for interval regression analysis
Information Sciences: an International Journal
Uncovering overlapping cluster structures via stochastic competitive learning
Information Sciences: an International Journal
Hi-index | 0.07 |
Motivated by the successes of large margin principle in classification learning, the maximum margin clustering method (MMC) received intensive attention recently. It seeks a decision function and cluster labels for data simultaneously such that a supervised SVM trained on the label-assigned data could achieve the maximum margin. MMC assigns a unique cluster label for each instance. However, in real applications, the data distributions from different clusters are usually overlapped, and thus an instance might belong to multiple clusters with certain probabilities. Several soft clustering methods, which make use of soft membership assignment, have been developed in literature and lead to better data partition than their label-assignment counterparts. It motivates us to develop a novel Soft Large Margin Clustering (SLMC for short hereafter) method. SLMC enjoys the advantages of both MMC and the soft clustering methods, i.e., on one hand, it possesses a decision function with the maximal margin between clusters, and on the other hand, it accomplishes soft assignments for each instance to individual clusters to capture the nature of data structure. Its algorithmic implementation follows an alternating iterative strategy, in which each step in the iteration generates a closed-form solution, and the convergence of the whole iteration process can be theoretically guaranteed. Experiments on both synthetic and real datasets verify the effectiveness of SLMC.