Scalable and Visualization-oriented Clustering for Exploratory Spatial Analysis

Clustering can be applied to many fields including data mining, statistical data analysis, pattern recognition, image processing etc. In the past decade, a lot of efficient and effective new clustering algorithms have been proposed, in which famous algorithms contributed from the database community are CLARANS, BIRCH, DBSCAN, CURE, STING, CLIGUE and WaveCluster. All these algorithms try to challenge the problem of handling huge amount of data in large-scale databases. In this paper, we propose a scalable and visualization-oriented clustering algorithm for exploratory spatial analysis (CAESA). The context of our research is 2D spatial data analysis, but the method can be extended to higher dimensional space. Here, “Scalable” means our algorithm can run focus-changing clustering in an efficient way, and “Visualization-oriented” indicates that our algorithm is adaptable to the visualization situation, that is, choosing appropriate clustering granularity automatically according to current visualization resolution. Experimental results show that our algorithm is effective and efficient.