基于线性判别分析和二分K均值的高维数据自适应聚类方法
Adaptive clustering method based on linear discriminant analysis and bisecting K-means for high dimensional data
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摘要: 将线性判别分析和二分K均值聚类耦合在一起,提出了一个适合于高维数据聚类的自适应方法:利用线性判别分析将高维数据集变换成低维数据集,然后在低维数据集上执行二分K均值聚类,并把得到的聚类结果通过一个簇成员指示矩阵H变换到原数据集中.将这样的过程反复进行,直到自适应地得到-个最优结果.基于现实数据集的实验结果证明了该方法的有效性.
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关键词:
- 维归约 /
- 线性判别分析 /
- 二分K均值 /
- 高维数据自适应聚类方法
Abstract: Combining linear discriminant analysis(LDA) and bisecting K-means clustering(BKM),an adaptively clustering method was proposed for high dimensional data.The method uses LDA to transform the high dimensional dataset into low dimensional one,applies BKM on the low dimensional dataset,and constructs the clusters in the original high dimensional dataset.The method is adaptively executed to generate the best result.Extensive experimental results on real-world datasets showed the effectiveness of the approach. -
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