利用特征重标度因子恢复含噪声特征数据集的聚类数
Recovering the number of clusters in data sets with noise features using feature rescaling factors
摘要：
在本文中，我们介绍了三种方法重新缩放数据集，旨在提高聚类有效性指标的可能性，返回的真实数目的球形高斯聚类与额外的噪声功能。我们的方法获得的功能重新缩放因子考虑到给定的数据集的结构和直观的想法，不同的功能可能有不同程度的相关性在不同的集群。我们实验与剪影（使用平方欧几里德，曼哈顿，和p $^{th}$权力的闵可夫斯基距离），邓恩的，卡林斯基-Harabasz和Hartigan指数的数据集与球形高斯集群和没有噪声功能。我们的结论是，我们的方法确实增加了估计数据集中簇的真实数量的机会。
英文摘要：
In this paper we introduce three methods for re-scaling data sets aiming at improving the likelihood of clustering validity indexes to return the true number of spherical Gaussian clusters with additional noise features. Our method obtains feature re-scaling factors taking into account the structure of a given data set and the intuitive idea that different features may have different degrees of relevance at different clusters. We experiment with the Silhouette (using squared Euclidean, Manhattan, and the p$^{th}$ power of the Minkowski distance), Dunn's, Calinski-Harabasz and Hartigan indexes on data sets with spherical Gaussian clusters with and without noise features. We conclude that our methods indeed increase the chances of estimating the true number of clusters in a data set.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
