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Efficient Cluster Labeling for Support Vector Clustering
Nov. 2013 (vol. 25 no. 11)
pp. 2494-2506
V. D'Orangeville, University of Sherbrooke, Sherbrooke
M. Andre Mayers, University of Sherbrooke, Sherbrooke
M. Ernest Monga, University of Sherbrooke, Sherbrooke
M. Shengrui Wang, University of Sherbrooke, Sherbrooke
We propose a new efficient algorithm for solving the cluster labeling problem in support vector clustering (SVC). The proposed algorithm analyzes the topology of the function describing the SVC cluster contours and explores interconnection paths between critical points separating distinct cluster contours. This process allows distinguishing disjoint clusters and associating each point to its respective one. The proposed algorithm implements a new fast method for detecting and classifying critical points while analyzing the interconnection patterns between them. Experiments indicate that the proposed algorithm significantly improves the accuracy of the SVC labeling process in the presence of clusters of complex shape, while reducing the processing time required by existing SVC labeling algorithms by orders of magnitude.
Index Terms:
Labeling,Clustering algorithms,Algorithm design and analysis,Static VAr compensators,Accuracy,Support vector machines,Kernel,mining methods and algorithms,Clustering,data mining
V. D'Orangeville, M. Andre Mayers, M. Ernest Monga, M. Shengrui Wang, "Efficient Cluster Labeling for Support Vector Clustering," IEEE Transactions on Knowledge and Data Engineering, vol. 25, no. 11, pp. 2494-2506, Nov. 2013, doi:10.1109/TKDE.2012.190
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