| 基于优化初始中心点的K-means文本聚类算法 |
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| 引用本文： | 张世博.基于优化初始中心点的K-means文本聚类算法[J].计算机与数字工程,2011(10):30-31. |
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| 作者姓名： | 张世博 |
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| 作者单位： | 北京石油化工学院计算机系 北京 102600 |
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| 摘 要： | K-means算法终止于一个局部最优状态,所以初始中心点的选择会在很大程度上影响其聚类效果.该文针对K-means算法所存在的问题,提出了一种优化初始中心点的算法.实验表明可以有效减少迭代次数并提高聚类精度,最终获得较好的聚类效果.
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| 关 键 词： | K均值 聚类 初始中心点 |
A K-means Text Clustering Algorithm Based on Optimizing Initial Points |
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| Zhang Shibo.A K-means Text Clustering Algorithm Based on Optimizing Initial Points[J].Computer and Digital Engineering,2011(10):30-31. |
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| Authors: | Zhang Shibo |
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| Affiliation: | Zhang Shibo(Department of Computer Science,Beijing Institute of Petrochemical Technology,Beijing 102600) |
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| Abstract: | K-means algorithm terminates at a local optimum,it is sensitive to initial starting condition.An improved algorithm is proposed,compared with the traditional algorithms,the proposed algorithm can get initial centers with higher quality.The method can achieve higher clustering accuracies. |
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| Keywords: | K-means clustering initial center point |
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