Abstract: Subspace clustering is a kind of effective strategy to high-dimensional data clustering, the principle of subspace clustering is as well as possible keeping original data information, meanwhile as small as possible using subspace to data clustering. Based on the studying of the existing soft subspace clustering, it proposes a new algorithm for subspace searching. The algorithm combines with the size of cluster and information entropy, defines a new subspace dimensional weight distribution mode, and then uses the feature vector of cluster subspace to measure the similarity of two clusters. It uses the idea of agglomerative hierarchical clustering in hierarchical clustering to data clustering, which overcoming the shortcomings of using information entropy or traditional similarity separately. Through the test in the Zoo, Votes, Soybean three typical categorical data set to find out that compared with other algorithms, the proposed algorithm not only can improve the accuracy of clustering, but also has the very high stability.

Key words: high-dimensional data, clustering, subspace, information entropy, hierarchical clustering

摘要： 子空间聚类是高维数据聚类的一种有效手段，子空间聚类的原理就是在最大限度地保留原始数据信息的同时用尽可能小的子空间对数据聚类。在研究了现有的子空间聚类的基础上，引入了一种新的子空间的搜索方式，它结合簇类大小和信息熵计算子空间维的权重，进一步用子空间的特征向量计算簇类的相似度。该算法采用类似层次聚类中凝聚层次聚类的思想进行聚类，克服了单用信息熵或传统相似度的缺点。通过在Zoo、Votes、Soybean三个典型分类型数据集上进行测试发现：与其他算法相比，该算法不仅提高了聚类精度，而且具有很高的稳定性。

关键词: 高维数据, 聚类, 子空间, 信息熵, 层次聚类