GeoMiner: a system prototype for spatial data mining
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Fuzzy Relative Position Between Objects in Image Processing: A Morphological Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Updated Bibliography of Temporal, Spatial, and Spatio-temporal Data Mining Research
TSDM '00 Proceedings of the First International Workshop on Temporal, Spatial, and Spatio-Temporal Data Mining-Revised Papers
Discovering Association Rules Based on Image Content
ADL '99 Proceedings of the IEEE Forum on Research and Technology Advances in Digital Libraries
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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.