Multi-representation Feature Tree and Spatial Clustering Algorithm
多代表点特征树与空间聚类算法

Spatial data have the features of largeness, complexity, continuity, spatial autocorrelation, missing data and error in spatial database. These characters require that a good spatial clustering algorithm must be high efficient, and should be able to detect clusters of complicated shapes, and the dusters found should be independent of the order in which the points in the space are examined, and should be not be impacted by outliers. The existed algorithms can not work well, Clustering algorithm based on multi-representation feature tree named CAMFT is proposed, A new data structure is firstly proposed to condense data, which drew the strongpoint from BIRCH algorithm and CURE algorithm, and then the algorithm that included the idea of random sampling is proposed to enhance the ability to detect very large data, As well as, the multi-representation feature tree can keep clusters of complicated shapes, so it can be used to detect spatial clusters. Experimental results show the algorithm can identify clusters of complicated shapes efficiently in large spatial database that have many outliers, and outperform BIRCH algorithm and CURE algorithm in efficiency.