A Crossover-Imaged Clustering Algorithm with Bottom-up Tree
作者： Chung-I Chang , Nancy P. Lin
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摘要: The grid-based clustering algorithms are efficient with low computation time, but the size of predefined grids and threshold significant cells seriously influenced their effects. ADCC [1] ACICA+ [2] two new algorithms. algorithm uses axis-shifted strategy cell twice to reduce influences inherits advantage time complexity. And crossover image just only one clustering. But extension original in is not easy find what else clusters it belongs to. crossover-imaged bottom-up tree architecture, called CIC-BTA, proposed use architecture have same results. main idea CIC-BTA link be pre-clusters combine into by using semi-significant final set result.
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