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摘要: K-均值聚类算法的执行时间过度依赖于初始点的选取,但是在实际问题中并不知道k的取值和怎样才能有效地选取初始点。在对K-均值算法中初始点的选取进行深入研究的基础上,提出了一种有效的初始点选取算法。现存的类间相似度并不能很好地度量两个类的相似性,为此提出了一种新颖的度量方法:类间影响因子,使用类间影响因子对类进行合并。该方法和上面提出的初始点选取算法能够根据数据集本身的特性快速地自动选取初始中心并给出初始点的个数。最后用高斯数据集对算法进行测试,得到了一个令人满意的结果。
中图分类号: