| A Fast Algorithm for Support Vector Clustering |
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| 引用本文： | 吕常魁,姜澄宇,王宁生. A Fast Algorithm for Support Vector Clustering[J]. 西南交通大学学报(英文版), 2004, 12(2): 136-140 |
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| 作者姓名： | 吕常魁 姜澄宇 王宁生 |
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| 作者单位： | CIMSResearchCenter,NanjingUniversityofAeronautics＆Astronautics,Nanjing210016,China |
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| 基金项目： | TheNationalHighTechnologyResearchandDevelopmentProgramofChina (No .86 3 5 11 930 0 0 9) |
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| 摘 要： | Support Vector Clustering (SVC) is a kernel-based unsupervised learning clustering method. The main drawback of SVC is its high computational complexity in getting the adjacency matrix describing the connectivity for each pairs of points. Based on the proximity graph model, the Euclidean distance in Hilbert space is calculated using a Gaussian kernel, which is the right criterion to generate a minimum spanning tree using Kruskal‘s algorithm. Then the connectivity estimation is lowered by only checking the linkages between the edges that construct the main stem of the MST ( Minimum Spanning Tree), in which the non-compatibility degree is originally defined to support the edge selection during linkage estimations. This new approach is experimentally analyzed.The results show that the revised algorithm has a better performance than the proximity graph model with faster speed, optimized clustering quality and strong ability to noise suppression, which makes SVC scalable to large data sets.
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| 关 键 词： | SVC 支持向量聚类 支持向量机 邻近曲线 最小生成树 |
A Fast Algorithm for Support Vector Clustering |
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| Abstract: | Support Vector Clustering (SVC) is a kernel-based unsupervised learning clustering method. The main drawback of SVC is its high computational complexity in getting the adjacency matrix describing the connectivity for each pairs of points. Based on the proximity graph model [3], the Euclidean distance in Hilbert space is calculated using a Gaussian kernel, which is the right criterion to generate a minimum spanning tree using Kruskal's algorithm. Then the connectivity estimation is lowered by only checking the linkages between the edges that construct the main stem of the MST (Minimum Spanning Tree), in which the non-compatibility degree is originally defined to support the edge selection during linkage estimations. This new approach is experimentally analyzed. The results show that the revised algorithm has a better performance than the proximity graph model with faster speed, optimized clustering quality and strong ability to noise suppression, which makes SVC scalable to large data sets. |
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| Keywords: | Support vector machines Support vector clustering Proximity graph Minimum spanning tree |
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