...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Journal of Emerging Technologies in Web Intelligence >Careful Seeding Method based on Independent Components Analysis for k-means Clustering
【24h】

Careful Seeding Method based on Independent Components Analysis for k-means Clustering

机译：基于独立成分分析的k-均值聚类细心播种方法

获取原文
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

—The k-means clustering method is a widely used clustering technique for the Web because of its simplicity and speed. However, the clustering result depends heavily on the chosen initial clustering centers, which are uniformly chosen at random from the data points. We propose a seeding method that is based on the independent component analysis for the k-means clustering method. We evaluate the performance of our proposed method and compare it with other seeding methods by using benchmark datasets. We also applied our proposed method to a Web corpus, which was provided by ODP, and the CLUTO datasets. The results from the experiments showed that the normalized mutual information of our proposed method is better than the normalized mutual information of the k-means clustering method, the KKZ method, and the k-means++ clustering method.
机译：-k均值聚类方法因其简单性和速度而成为Web上广泛使用的聚类技术。但是，聚类结果在很大程度上取决于所选的初始聚类中心，这些中心是从数据点随机选择的。我们针对k均值聚类方法提出了一种基于独立成分分析的播种方法。我们评估了我们提出的方法的性能，并通过使用基准数据集将其与其他播种方法进行了比较。我们还将我们提出的方法应用于由ODP提供的Web语料库以及CLUTO数据集。实验结果表明，本文提出的方法的归一化互信息优于k均值聚类，KKZ方法和k-means ++聚类的归一化互信息。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号