A Spatial Clustering Algorithm Based on SOFM
This paper analyses some important characteristics of self-organization map network. Based on this analysis, we propose a method that can overcome the insufficiencies of single self-organization feature map (SOFM) network. The implementation detail of our proposed self-organizing feature map network algorithm is also discussed. Our proposed algorithm has a number of advantages. It can overcome the insufficiencies identified in other similar clustering algorithms. It is able to find clusters in different shapes and is insensitive to input data sequence. It can process noisy and multi-dimensional data well in multi-resolutions. Furthermore the proposed clustering method can find the dense or sparse areas with different data distributions. It will be convenient to discover the distribution mode and interesting relationship among data. We have conducted numerous experiments in order to justify this novel ideal of spatial data clustering. It has been shown that the proposed method can be applied to spatial clustering well.
KeywordsCluster Number Sparse Area Propose Cluster Method Temporal Data Mining Input Data Sequence
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