一种结合GAAC和K-means的维吾尔文文本聚类算法

Combined algorithm of GAAC and K-means for Uyghur text clustering

介绍了K-means和GAAC聚类算法思想和两种特征提取方法对维吾尔文文本表示及聚类效率的影响。在较大规模文本语料库基础上,分别用K-means和GAAC的方法进行维吾尔文文本聚类实验及性能对比分析,针对经典K-means算法对初始聚类中心的过分依赖性及不稳定性缺点以及GAAC的高计算复杂性,提出了一种结合GACC和K-means的维吾尔文聚类算法。本算法分两步完成聚类操作,首先是GAAC模块从少量文本集中获取最优的初始类中心,然后是K-means模块对大量文本集进行快速聚类。实验结果表明,新算法在聚类准确率和时间复杂度上都有了显著的提高。

The paper introduced the K-means method and the GAAC clustering method and the im- pact of two feature extraction methods on Uyghur text representation and clustering efficiency. Based on the large-scale text corpus, both the K-means method and the GAAC clustering method were used to carry out Uyghur text clustering experiments and do performance comparative analysis. In view of the shortcoming that the K-means method is over dependent on the initial cluster centers and instable as well as the high computational complexity of the GAAC method, this paper proposed a Uyghur text cluste- ring algorithm combining the GAAC and the K-means methods. The proposed algorithm has two steps. Firstly, the optimal initial cluster center is obtained from the small amount of text set by the GAAC method. Secondly, the large amount of text set is fast clustered by the K-means method. Experimental results show that the proposed algorithm has a significant increase on the clustering accuracy and the time complexity.