Clustering algorithms: A comparative approach

Author(s)

Rodriguez, Mayra Z. ^[1] ; Comin, Cesar H. ^[2] ; Casanova, Dalcimar ^[3] ; Bruno, Odemir M. ^[4] ; Amancio, Diego R. ^[1] ; Costa, Luciano da F. ^[4] ; Rodrigues, Francisco A. ^[1]

Affiliation

^[1] Univ Sao Paulo, Inst Math & Comp Sci, Sao Carlos, SP - Brazil

^[2] Univ Fed Sao Carlos, Dept Comp Sci, Sao Carlos, SP - Brazil

^[3] Fed Univ Technol, Curitiba, Parana - Brazil

^[4] Univ Sao Paulo, Sao Carlos Inst Phys, Sao Carlos, SP - Brazil

• Document type: Journal article
• Source: PLoS One; v. 14, n. 1 JAN 15 2019.
• Web of Science Citations: 8

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. (AU)

• FAPESP's process: 16/19069-9 - Using semantical information to classify texts modelled as complex networks
• Grantee: Diego Raphael Amancio
• Support Opportunities: Regular Research Grants
• FAPESP's process: 14/20830-0 - Using complex networks to recognize patterns in written texts
• Grantee: Diego Raphael Amancio
• Support Opportunities: Regular Research Grants
• FAPESP's process: 15/18942-8 - Associating Complex Networks with Effective Feature Spaces
• Grantee: Cesar Henrique Comin
• Support Opportunities: Scholarships in Brazil - Post-Doctoral
• FAPESP's process: 15/22308-2 - Intermediate representations in Computational Science for knowledge discovery
• Grantee: Roberto Marcondes Cesar Junior
• Support Opportunities: Research Projects - Thematic Grants
• FAPESP's process: 14/08026-1 - Artificial vision and pattern recognition applied to vegetal plasticity
• Grantee: Odemir Martinez Bruno
• Support Opportunities: Regular Research Grants
• FAPESP's process: 18/09125-4 - Representation, characterization and modeling of biological images using complex networks
• Grantee: Cesar Henrique Comin
• Support Opportunities: Regular Research Grants
• FAPESP's process: 11/50761-2 - Models and methods of e-Science for life and agricultural sciences
• Grantee: Roberto Marcondes Cesar Junior
• Support Opportunities: Research Projects - Thematic Grants