数据变换对聚类算法影响的实验分析

Experimental Analysis About Influence of Variable Transformation Upon Clustering Algorithm

对于基于距离的聚类算法,数据变换是实现成功挖掘的重要保证。实验结果表明,改变变量值域区间跨度的大小对数据对象分布形态有明显的影响,从而也改变了聚类结果;数据变换能够提高聚类结果的有效性和准确率。

The variable transformation is the important guarantee of successful data mining for clustering algorithm based on distance similarity. Experimental analysis shows that variable transformation can change data distribution state through adjustment of the range of values, and improve the effectiveness and accuracy of clustering results.