随着多媒体技术的发展，许多领域产生大量的高维数据集。为了有效地检索这些高维数据，高维索引成为人们研究的热点。聚类树是一种有效地支持高维数据检索的索引结构。提出了一种基于子空间聚类的聚类树结构，该索引结构基于一种改进的CLIQUE聚类算法，利用小波变换的多尺度特性对图像特征分布曲线进行不同尺度的小波变换，去除一些小的分类和可能的噪声干扰，从而得到不同粒度下的层次聚类。在层次聚类的基础上，建立起分层索引结构。由于改进的聚类算法使用爬山法确定子空间聚类，因而有效地避免了用户参数的定义。实验结果证明，该方法在不需要用户设定聚类参数下能够进行有效聚类，在不同尺度下构建的聚类结构能够有效地组织图像关系，大大提高图像的检索效率。

Nowadays large volumes of data with high dimensionality are being generated in many fields. Many approaches have been proposed to index high-dimensional datasets for efficient querying. ClusterTree is a new indexing approach representing clusters generated by any existing clustering approach. Lots of clustering algorithms have been developed, and in most of them some parameters should be determined manually. The authors propose a new subspace-cluster indexing algorithm, which based on the improved CLIQUE and avoids bias on any parameters caused by user. Using multi-resolution property of wavelet transforms to reprocess the distribution curve of samples, the proposed approach can cluster at different resolution and remain the relation between these clusters to construct hierarchical index. The results of the experiment confirm that the subspace-cluster algorithm is very applicable and efficient, and show that this hierarchical indexing structure does well in the content-based image retrieval.