中文摘要：

针对特征权重自调节软子空间聚类（softsubspaceclusteringwithfeatureweightself-adjustmentmecha-nism，SC-FWSA)算法使用欧氏距离，存在对数据适应性较差的问题，将SC-FWSA算法中的欧氏距离拓展为闵科夫斯基距离（Minkowskidistance)，提出一种基于闵科夫斯基距离的特征权重自调节软子空间聚类（Minkowskidistancebasedsoftsubspaceclusteringwithfeatureweightself-adjustmentmechanism,MSC-FWSA)算法，MSC-FWSA算法有效提高了SC-FWSA算法对数据的适应性。若干真实数据集上的对比性实验验证了MSC-FWSA算法的有效性。

英文摘要：

Seeing that SC-FWSA clustering algorithm used Euclidean distance and had a poor adaptability to data,this paper extended Euclidean distance in SC-FWSA clustering algorithm to Minkowski distance, and proposed a Minkowski distance based soft subspace clustering with feature weight self-adjustment mechanism( MSC-FWSA) . MSC-FWSA improves the adaptability of the SC-FWSA algorithm to data. Comparative experiments presented on some real data sets show the effectiveness of the MSC-FWSA algorithm.