An adaptive algorithm for clustering cumulative probability distribution functions using the Kolmogorov–Smirnov two-sample test

•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. This paper proposes an adaptive a...

Ausführliche Beschreibung

Bibliographische Detailangaben

Veröffentlicht in:	Expert systems with applications Jg. 42; H. 8; S. 4016 - 4021
Hauptverfasser:	Mora-López, Llanos, Mora, Juan
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier Ltd 15.05.2015
Schlagworte:	Adaptive algorithms Adaptive clustering Algorithms Centroids Clustering Clusters Cumulative probability distribution functions Kolmogorov–Smirnov two-sample test Probability distribution functions Statistical methods Statistics Kolmogorov–Smirnov two-sample test Cumulative probability distribution functions Adaptive clustering
ISSN:	0957-4174, 1873-6793
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!