Silhouettes: a graphical aid to the interpretation and validation of cluster analysis
Journal of Computational and Applied Mathematics
Ant-Based Clustering and Topographic Mapping
Artificial Life
Density Based Clustering with Crowding Differential Evolution
SYNASC '05 Proceedings of the Seventh International Symposium on Symbolic and Numeric Algorithms for Scientific Computing
A genetic rule-based data clustering toolkit
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
An efficient hybrid approach based on PSO, ACO and k-means for cluster analysis
Applied Soft Computing
Guiding users within trust networks using swarm algorithms
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
A unifying criterion for unsupervised clustering and feature selection
Pattern Recognition
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Clustering is a fundamental and hence widely studied problem in data analysis. In a multi-objective perspective, this paper combines principles from two different clustering paradigms: the connectivity principle from density-based methods is integrated into the partitional clustering approach. The standard k-Means algorithm is hybridized with Particle Swarm Optimization. The new method (PSO-kMeans) benefits from both a local and a global view on data and alleviates some drawbacks of the k-Means algorithm; thus, it is able to spot types of clusters which are otherwise difficult to obtain (elongated shapes, non-similar volumes). Our experimental results show that PSO-kMeans improves the performance of standard k-Means in all test cases and performs at least comparable to state-of-the-art methods in the worst case. PSO-kMeans is robust to outliers. This comes at a cost: the preprocessing step for finding the nearest neighbors for each data item is required, which increases the initial linear complexity of k-Means to quadratic complexity.