Clustering algorithms: A comparative approach

By Mayra Zegarra Rodriguez, Cesar Henrique Comin, Dalcimar Casanova, Odemir Martinez Bruno, Diego Raphael Amancio, Francisco Aparecido Rodrigues, Luciano da Fontoura Costa. PLoS ONE, 2016

543 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/3140c7a9bddcb447fdefb6ce5f43fc382fe6c9fb

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In Artificial Intelligence

  1. Joaquim R. Martins
  2. Ji-chao Li
  3. Xiaosong Du
  4. Gaetan K. W. Kenway

In Information Systems

  1. Joaquim R. Martins
  2. Ji-chao Li
  3. Xiaosong Du

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