Clustering algorithms: A comparative approach

Many real-world systems can be studied in terms of pattern recognition tasks, so that proper use (and understanding) of machine learning methods in practical applications becomes essential. While many classification methods have been proposed, there is no consensus on which methods are more suitable...

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Veröffentlicht in:	PloS one Jg. 14; H. 1; S. e0210236
Hauptverfasser:	Rodriguez, Mayra Z., Comin, Cesar H., Casanova, Dalcimar, Bruno, Odemir M., Amancio, Diego R., Costa, Luciano da F., Rodrigues, Francisco A.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States Public Library of Science 15.01.2019 Public Library of Science (PLoS)
Schlagworte:	Algorithms Artificial intelligence Authorship Biology and Life Sciences Classification Cluster Analysis Clustering Clustering (Computers) Comparative analysis Computer and Information Sciences Computer science Configurations Data analysis Data mining Datasets Discriminant analysis Humans Knowledge discovery Language Learning algorithms Machine learning Machine Learning - trends Mathematics Methods Normal Distribution Parameter sensitivity Pattern recognition Performance enhancement Physical Sciences Research and Analysis Methods Sensitivity analysis Social Sciences Statistical mechanics Subsidies Brazil
ISSN:	1932-6203, 1932-6203
Online-Zugang:	Volltext
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