Flexible Multi-Objective Particle Swarm Optimization Clustering with Game Theory to Address Human Activity Discovery Fully Unsupervised

Most research in human activity recognition is supervised, while non-supervised approaches are not completely unsupervised. Moreover, These methods cannot be used in real-time applications due to high calculations. In this paper, we provide a novel flexible multi-objective particle swarm optimization clustering method based on game theory (FMOPG) to discover human activities fully unsupervised. Unlike conventional clustering methods that estimate the number of clusters and are very time-consuming and inaccurate, an incremental technique is introduced which makes the proposed method flexible in dealing with the number of clusters and improves the speed of clustering. By adopting this technique, clusters with a better connectedness and good separation from other clusters are gradually selected. Updating of particles’ velocity is modified by adopting the concept of mean-shift vector to improve the convergence speed of PSO in achieving the best solution and dealing with non-spherical shape clusters. Multi-objective optimization problem is mapped to game theory by adopting Nash equilibrium to select the optimal solution on the pareto front. Gaussian mutation is also employed on the pareto front to generate diverse solutions and create a balance between exploitation and exploration. Moreover, A smart grid-based method is proposed to initialize the population to generate diverse solutions and reduce the variance between the worst and best clustering results. The proposed method is compared with state-of-the-art methods on seven challenging datasets. FMOPG has improved clustering accuracy by 3.65 % compared to other automated methods. Moreover, the incremental technique has improved the clustering time by 71.18 %.