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.
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