An adaptive algorithm for clustering cumulative probability distribution functions using the Kolmogorov–Smirnov two-sample test
作者：
Highlights：
• An adaptive clustering algorithm has been proposed.
• The measure distance proposed is Kolmogorov–Smirnov statistics.
• A practical application of the algorithm proves its power.
• The proposed algorithm allows better clustering solar spectra data than classical k-means.
摘要
•An adaptive clustering algorithm has been proposed.•The measure distance proposed is Kolmogorov–Smirnov statistics.•A practical application of the algorithm proves its power.•The proposed algorithm allows better clustering solar spectra data than classical k-means.
论文关键词：Adaptive clustering,Cumulative probability distribution functions,Kolmogorov–Smirnov two-sample test
论文评审过程：Available online 16 January 2015.
论文官网地址：https://doi.org/10.1016/j.eswa.2014.12.027
