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A fast two-class classifier for 2D data using complex-moment-preserving principle
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DOI：10.1016/0031-3203(95)00103-4.png)
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A new moment-preserving classifier for two-class clustering is suggested. Based on preserving the complex moments of two-dimensional (2D) input data, an analytic, non-iterative and unsupervised classifier is proposed. This new classifier is suitable for applications requiring fast automatic two-class clustering of 2D data or fast automatic hierarchical clustering. Furthermore, the computation time is of order of data size and hence much faster than the well known iterative k-means algorithm. Experimental results show that the proposed classifier can acquire acceptable clustering results.
Keyword:
clustering
patterns
complex moments
moment-preserving principle
two-class classifier
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期刊
IF:
7.6
论文数:
1.3W
被引数:
4.5W
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