Cluster analysis and mathematical programming
Mathematical Programming: Series A and B - Special issue: papers from ismp97, the 16th international symposium on mathematical programming, Lausanne EPFL
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Interval Set Clustering of Web Users with Rough K-Means
Journal of Intelligent Information Systems
On fuzzy-rough sets approach to feature selection
Pattern Recognition Letters
Invariances in kernel methods: From samples to objects
Pattern Recognition Letters
Some refinements of rough k-means clustering
Pattern Recognition
Kernel methods and the exponential family
Neurocomputing
Survey of clustering algorithms
IEEE Transactions on Neural Networks
Rough kernel clustering algorithm with adaptive parameters
AICI'11 Proceedings of the Third international conference on Artificial intelligence and computational intelligence - Volume Part III
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By means of analyzing kernel clustering algorithm and rough set theory, a novel clustering algorithm, rough kernel k-means clustering algorithm, was proposed for clustering analysis. Through using Mercer kernel functions, samples in the original space were mapped into a highdimensional feature space, which the difference among these samples in sample space was strengthened through kernel mapping, combining rough set with k-means to cluster in feature space. These samples were assigned into up-approximation or low-approximation of corresponding clustering centers, and then these data that were in up-approximation and low-approximation were combined and to update cluster center. Through this method, clustering precision was improved, clustering convergence speed was fast compared with classical clustering algorithms The results of simulation experiments show the feasibility and effectiveness of the kernel clustering algorithm.