الگوریتم بهینه سازی ذرات بر پایه تراکم برای خوشه بندی داده

Particle swarm optimization (PSO) algorithm is widely used in cluster analysis. However, it is a stochastic technique that is vulnerable to premature convergence to sub-optimal clustering solutions. PSO-based clustering algorithms also require tuning of the learning coefficient values to find better solutions. The latter drawbacks can be evaded by setting a proper balance between the exploitation and exploration behaviors of particles while searching the feature space. Moreover, particles must take into account the magnitude of movement in each dimension and search for the optimal solution in the most populated regions in the feature space. This study presents a novel approach for data clustering based on particle swarms. In this proposal, the balance between exploitation and exploration processes is considered using a combination of (i) kernel density estimation technique associated with new bandwidth estimation method to address the premature convergence and (ii) estimated multidimensional gravitational learning coefficients. The proposed algorithm is compared with other state-of-the-art algorithms using 11 benchmark datasets from the UCI Machine Learning Repository in terms of classification accuracy, repeatability represented by the standard deviation of the classification accuracy over different runs, and cluster compactness represented by the average Dunn index values over different runs. The results of Friedman Aligned-Ranks test with Holm's test over the average classification accuracy and Dunn index values indicate that the proposed algorithm achieves better accuracy and compactness when compared with other algorithms. The significance of the proposed algorithm is represented in addressing the limitations of the PSO-based clustering algorithms to push forward clustering as an important technique in the field of expert systems and machine learning. Such application, in turn, enhances the classification accuracy and cluster compactness. In this context, the proposed algorithm achieves better results compared with other state-of-the-art algorithms when applied to high-dimensional datasets (e.g., Landsat and Dermatology). This finding confirms the importance of estimating multidimensional learning coefficients that consider particle movements in all the dimensions of the feature space. The proposed algorithm can likewise be applied in repeatability matters for better decision making, as in medical diagnosis, as proved by the low standard deviation obtained using the proposed algorithm in conducted experiments.