• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Information Processing & Management >An experimental study of constrained clustering effectiveness in presence of erroneous constraints
【24h】

An experimental study of constrained clustering effectiveness in presence of erroneous constraints

机译：存在错误约束条件下约束聚类有效性的实验研究

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Recently a new fashion of semi-supervised clustering algorithms, coined as constrained clustering, has emerged. These new algorithms can incorporate some a priori domain knowledge to the clustering process, allowing the user to guide the method. The vast majority of studies about the effectiveness of these approaches have been performed using information, in the form of constraints, which was totally accurate. This would be the ideal case, but such a situation will be impossible in most realistic settings, due to errors in the constraint creation process, misjudgements of the user, inconsistent information, etc. Hence, the robustness of the constrained clustering algorithms when dealing with erroneous constraints is bound to play an important role in their final effectiveness. In this paper we study the behaviour of four constrained clustering algorithms (Constrained k-Means, Soft Constrained k-Means, Constrained Normalised Cut and Normalised Cut with Imposed Constraints) when not all the information supplied to them is accurate. The experimentation over text and numeric datasets using two different noise models, one of them an original approach based on similarities, highlighted the strengths and weaknesses of each method when working with positive and negative constraints, indicating the scenarios in which each algorithm is more appropriate.
机译：最近，出现了一种新的半监督聚类算法，称为约束聚类。这些新算法可以将一些先验领域知识合并到聚类过程中，从而允许用户指导该方法。关于这些方法的有效性的绝大多数研究都是使用信息（以约束形式）进行的，该信息是完全准确的。这将是理想的情况，但是由于约束创建过程中的错误，用户的错误判断，不一致的信息等，在大多数实际设置中这种情况将是不可能的。因此，在处理时，约束聚类算法的鲁棒性错误的约束必将在其最终效力中发挥重要作用。当我们提供的信息不是全部准确时，我们将研究四种约束聚类算法（约束k均值，软约束k均值，约束归一化割和具有强加约束的归一化割）的行为。使用两种不同的噪声模型对文本和数字数据集进行的实验（其中一种是基于相似性的原始方法）突出显示了每种方法在使用正约束和负约束时的优缺点，指出了每种算法更合适的情况。

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