一种自底向上的高维聚类算法

Bottom-up Clustering Algorithm of High Dimension

基于小波变换的聚类算法是高效的,能够探测到任意形状的聚类,可成功去除孤立点,并对输入数据的顺序不敏感.但当聚类维数增长时,算法的有效性会降低,计算复杂度也相当可观.采用自底向上的思想对小波聚类算法进行改进,使之适合高维聚类,并将改进算法并行化以增强可伸缩性.实验表明改进算法并未影响聚类质量,而且可有效地进行高维聚类,并降低了计算复杂度.

Clustering algorithm based on wavelet transform is efficient, and which can detect clusters of arbitrary shape. It is insensitive to the outliers and the order of input data. However, efficiency of the algorithm would be degraded, and computation complexity of the algorithm would be considerable with increase of clustering dimensions. A bottom-u Pmethod is put forward to make the original algorithm fit to clustering in high dimension, and the scalability of the improved algorithm is enhanced by parallelization. The experiment demonstrates that the improved algorithm has no impact on quality of clustering and has a good efficient in high dimension clustering and in decrease of comnutation comnlexity.