Support vector clustering
Support vector clustering
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DOI：
10.1162/15324430260185565
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发表时间：
2002-03-01
影响因子：
6
通讯作者：
Vapnik, V
中科院分区：
文献类型：
--
作者：
Ben-Hur, A;Horn, D;Vapnik, V
提出了一种基于支持向量机的聚类方法。通过高斯核将数据点映射到高维特征空间，在高维特征空间中搜索最小封闭球。当映射回数据空间时，这个球体可以分成几个组件，每个组件包含一个单独的点簇。我们提出了一个简单的算法来识别这些集群。高斯核的宽度控制探测数据的规模，而软边缘常数有助于处理离群值和重叠聚类。通过改变这两个参数来探索数据集的结构，保持最小数量的支持向量以确保平滑的聚类边界。我们证明了我们的算法在几个数据集上的性能。
We present a novel clustering method using the approach of support vector machines. Data points are mapped by means of a Gaussian kernel to a high dimensional feature space, where we search for the minimal enclosing sphere. This sphere, when mapped back to data space, can separate into several components, each enclosing a separate cluster of points. We present a simple algorithm for identifying these clusters. The width of the Gaussian kernel controls the scale at which the data is probed while the soft margin constant helps coping with outliers and overlapping clusters. The structure of a dataset is explored by varying the two parameters, maintaining a minimal number of support vectors to assure smooth cluster boundaries. We demonstrate the performance of our algorithm on several datasets.