基于单元区域的高维数据聚类算法

Clustering Algorithm of High-dimensional Data Based on Unit Region

提出一种高维数据集合聚类算法(CAHD)。采用双向搜索策略在指定的n维空间或其子空间上发现数据点密集的单元区域,采用逐位相与的方法为这些密集单元区域聚类。双向搜索策略能够有效地减少搜索空间,提高算法效率,聚类密集单元区域只用到逐位与和位移2种机器指令。实验结果表明,在发现的类数量相同的情况下,CAHD算法的运行时间比其他算法减少30%。

This paper proposes a Clustering Algorithm of High-dimensional Data（CAHD）. Unit regions with intensive data points are found by employing the two-way search strategy in the designated n-dimensional space or its subspaces, and these intensive modules are clustered by a case-by-phase approach, Two-way search strategy can effectively reduce the search space, improve the efficiency of algorithms, and cluster intensive regional unit only uses one by one with two machines and displacement direction. Experimental results show that the running time CAHD algorithm spent is 30% less than other algorithms with the same number of categories found.