Fuzzy c-Means Clustering Using Transformations into High Dimensional Spaces
Fuzzy c-Means Clustering Using Transformations into High Dimensional Spaces
复制标题
使用高维空间变换的模糊 c 均值聚类
DOI：
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发表时间：
2002
期刊：
影响因子：
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通讯作者：
Daisuke Suizu
中科院分区：
文献类型：
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作者：
S. Miyamoto;Daisuke Suizu
研究了支持向量机中用于高维空间非线性变换的核模糊均值聚类算法。考虑了标准方法和熵基方法中的目标函数，推导了交替优化算法中的迭代解。这种方法一般不能得到数据空间中的显式聚类中心，但模糊分类函数是有用的，它比硬方法中的清晰聚类具有更多的信息。给出了径向基核函数的数值例子。
Algorithms of fuzzy -means clustering with kernels employed in nonlinear transformations into high dimensional spaces in the support vector machines are studied. The objective functions in the standard method and the entropy based method are considered and iterative solutions in the alternate optimization algorithm are derived. Explicit cluster centers in the data space are not obtained by this method in general but fuzzy classification functions are useful which have much more information than crisp clusters in the hard -means. Numerical examples using radial basis kernel functions are given.