A subspace clustering algorithm for high dimensional spatial data
一种高维空间数据的子空间聚类算法

Traditional grid clustering methods fail to consider the affect of neighbored grids, and may result in unsmoothed clustering, wrong judgement of clustering boundary, etc. A subspace clustering algorithm for high dimensional spatial data was proposed, which added the affect of neighbored grids when clustering. Experiment results show that this algorithm conquers the unsmoothed clustering and deals with clustering boundary well.