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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| Vydáno v: | PloS one Ročník 14; číslo 1; s. e0210236 |
|---|---|
| Hlavní autoři: | , , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
United States
Public Library of Science
15.01.2019
Public Library of Science (PLoS) |
| Témata: | |
| ISSN: | 1932-6203, 1932-6203 |
| On-line přístup: | Získat plný text |
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| Abstract | 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. |
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| AbstractList | 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. 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.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. |
| Audience | Academic |
| Author | Rodriguez, Mayra Z. Casanova, Dalcimar Comin, Cesar H. Bruno, Odemir M. Costa, Luciano da F. Amancio, Diego R. Rodrigues, Francisco A. |
| AuthorAffiliation | 2 Department of Computer Science, Federal University of São Carlos, São Carlos, São Paulo, Brazil 4 São Carlos Institute of Physics, University of São Paulo, São Carlos, São Paulo, Brazil 3 Federal University of Technology, Paraná, Paraná, Brazil 1 Institute of Mathematics and Computer Science, University of São Paulo, São Carlos, São Paulo, Brazil University of Ulm, GERMANY |
| AuthorAffiliation_xml | – name: 3 Federal University of Technology, Paraná, Paraná, Brazil – name: 2 Department of Computer Science, Federal University of São Carlos, São Carlos, São Paulo, Brazil – name: 4 São Carlos Institute of Physics, University of São Paulo, São Carlos, São Paulo, Brazil – name: University of Ulm, GERMANY – name: 1 Institute of Mathematics and Computer Science, University of São Paulo, São Carlos, São Paulo, Brazil |
| Author_xml | – sequence: 1 givenname: Mayra Z. surname: Rodriguez fullname: Rodriguez, Mayra Z. – sequence: 2 givenname: Cesar H. orcidid: 0000-0003-1207-4982 surname: Comin fullname: Comin, Cesar H. – sequence: 3 givenname: Dalcimar surname: Casanova fullname: Casanova, Dalcimar – sequence: 4 givenname: Odemir M. surname: Bruno fullname: Bruno, Odemir M. – sequence: 5 givenname: Diego R. surname: Amancio fullname: Amancio, Diego R. – sequence: 6 givenname: Luciano da F. surname: Costa fullname: Costa, Luciano da F. – sequence: 7 givenname: Francisco A. surname: Rodrigues fullname: Rodrigues, Francisco A. |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/30645617$$D View this record in MEDLINE/PubMed |
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| Title | Clustering algorithms: A comparative approach |
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