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Text clustering method based on the iteration convergence of initial centers

机译:基于初始中心迭代收敛的文本聚类方法

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In this paper according to the center of the text set and the initial cluster, a set of directions with good discriminations have been chosen to construct the IMIC coordinate. In this coordinate system re-scaling functions were constructed for each axis which are used to improve the effectiveness of the cluster policy. After several iterations, the IMIC algorithm finally converged to the final solution. The time complexity of the IMIC algorithm remains the same level as that of the K-means. Experimental results show that, the IMIC algorithm has better clustering quality.
机译:在本文中,根据文本集的中心和初始聚类,选择了一组具有良好判别力的方向来构造IMIC坐标。在该坐标系中,为每个轴构造了重新缩放功能,这些功能用于提高群集策略的有效性。经过多次迭代,IMIC算法最终收敛到最终解决方案。 IMIC算法的时间复杂度保持与K-means相同的水平。实验结果表明,IMIC算法具有更好的聚类质量。

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