Clustering Algorithms: A Comparative Approach (1612.08388v1)

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 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.

• The paper compares seven clustering algorithms implemented in R using artificial datasets with varying properties to evaluate their performance.
• A rigorous methodological framework is used, employing metrics like ARI and NMI, and evaluating performance under default parameters, single parameter variation, and random parameter sampling.
• Findings highlight the importance of parameter tuning and identify algorithms like spectral, subspace, and hcmodel methods as promising for different data characteristics, especially in high dimensions.

Comparative Analysis of Clustering Algorithms in R

The paper, authored by Mayra Z. Rodriguez et al., explores a systematic comparison of seven prominent clustering algorithms using the R programming language. This comparative paper is motivated by the need to understand and select suitable clustering methods for real-world applications, given the absence of a consensus on the most appropriate algorithms for classifying datasets with varying characteristics. Clustering, as an unsupervised learning technique, aims to identify and group objects into classes or clusters without prior knowledge of class labels—a task made challenging by the lack of universally accepted methods for diverse datasets.

Methodological Framework

The authors introduce a rigorous framework by employing artificial datasets that are methodically generated to possess various tunable properties. Such properties include the number of classes, feature dimensionality, and inter-class separation levels. This approach allows for a controlled environment to test the clustering methods, providing a robust basis for performance evaluation across different datasets.

The paper focuses on seven clustering algorithms implemented in the R language: k-means, clara (Clustering for Large Applications), hierarchical clustering, expectation-maximization (EM), hcmodel-based clustering (a Gaussian mixture approach), spectral clustering, and subspace clustering. Each of these algorithms represents a family of clustering methods, ranging from partitional to hierarchical strategies, and encompasses a variety of approaches such as model-based and spectral methods.

Performance Metrics and Evaluation

The performance of these algorithms is predominantly assessed using traditional indices like the Adjusted Rand Index (ARI), Jaccard Index, Fowlkes-Mallows Index, and Normalized Mutual Information (NMI). These indices facilitate a quantitative evaluation of how well the clustering results align with the known partitions of the datasets.

The evaluation is structured in three phases:

1. Default Parameters: The initial analysis involves assessing the algorithms with their default parameter settings. This provides insight into the out-of-the-box performance researchers might expect when utilizing these methods without customizing the parameters.
2. One-Dimensional Parameter Variation: This phase involves the systematic variation of a single parameter at a time while keeping others fixed, revealing the sensitivity of each algorithm to its hyperparameters. The findings suggest that some algorithms, like the EM and hierarchical clustering methods, are notably sensitive to specific parameter changes.
3. Random Sampling of Parameters: To gauge the algorithmic potential when optimally tuned, parameters are sampled randomly within predefined ranges. This analysis highlights the spectral method's superior performance in high-dimensional contexts, although the hcmodel and subspace clustering methods can outperform under certain conditions.

Implications and Future Directions

The paper's findings underscore the importance of parameter tuning in clustering methods—a practice that can substantially enhance classification accuracy. While the spectral method and subspace clustering show promise in handling high-dimensional data effectively, the hcmodel-based approach provides versatility with the right parameter settings. The paper's comprehensive methodology establishes a benchmark for evaluating clustering algorithms and their implementation in R, accommodating researchers with varying expertise in data clustering tasks.

Future developments in clustering algorithm research could leverage this paper's insights to refine parameter optimization strategies, potentially integrating metaheuristic approaches for automatic parameter tuning. Moreover, extending the comparative analysis to include a broader range of real-world and synthetic datasets could further validate the algorithms' adaptability and robustness in diverse application scenarios.

Conclusion

The paper presents a detailed comparative analysis of clustering algorithms in R, offering valuable empirical insights into their performance across different types of datasets. Such an evaluation is crucial for guiding the selection of clustering algorithms in data-rich disciplines, ultimately enhancing the efficacy of data-driven interpretations and decisions.