Clustering Algorithms: A Comparative Approach
This work provides guidance for practitioners in choosing clustering algorithms, but it is incremental as it compares existing methods without introducing new ones.
The paper tackled the problem of selecting suitable clustering methods for given datasets by systematically comparing 7 well-known clustering algorithms in R, using artificial datasets with tunable properties; the results showed that the spectral approach usually outperformed others with default configurations, and random parameter selection improved performance when defaults were inaccurate.
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 a myriad of 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 7 well-known clustering methods available in the R language. 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 usually outperformed the other clustering algorithms. We also found that the default configuration of the adopted implementations was not 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.