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Clustering algorithms: A comparative approach

Rodriguez, Mayra Z ; Comin, Cesar H ; Casanova, Dalcimar ; Bruno, Odemir M ; Amancio, Diego R ; Costa, Luciano da F ; Rodrigues, Francisco A Kestler, Hans A

PloS one, 2019-01, Vol.14 (1), p.e0210236-e0210236 [Periódico revisado por pares]

United States: Public Library of Science

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Título:
Clustering algorithms: A comparative approach
Autor: Rodriguez, Mayra Z ; Comin, Cesar H ; Casanova, Dalcimar ; Bruno, Odemir M ; Amancio, Diego R ; Costa, Luciano da F ; Rodrigues, Francisco A
Kestler, Hans A
Assuntos: 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
É parte de: PloS one, 2019-01, Vol.14 (1), p.e0210236-e0210236
Notas: ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
Competing Interests: The authors have declared that no competing interests exist.
Descrição: 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 for a given dataset. As a consequence, it is important to comprehensively compare methods in many possible scenarios. In this context, we performed a systematic comparison of 9 well-known clustering methods available in the R language assuming normally distributed data. In order to account for the many possible variations of data, we considered artificial datasets with several tunable properties (number of classes, separation between classes, etc). In addition, we also evaluated the sensitivity of the clustering methods with regard to their parameters configuration. The results revealed that, when considering the default configurations of the adopted methods, the spectral approach tended to present particularly good performance. We also found that the default configuration of the adopted implementations was not always accurate. In these cases, a simple approach based on random selection of parameters values proved to be a good alternative to improve the performance. All in all, the reported approach provides subsidies guiding the choice of clustering algorithms.
Editor: United States: Public Library of Science
Idioma: Inglês

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Clustering algorithms: A comparative approach

Rodriguez, Mayra Z ; Comin, Cesar H ; Casanova, Dalcimar ; Bruno, Odemir M ; Amancio, Diego R ; Costa, Luciano da F ; Rodrigues, Francisco A Kestler, Hans A

United States: Public Library of Science

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