A Particle Swarm Clustering Algorithm Based on Tree Structure and Neighborhood

Cluster analysis is one of the important research contents in data mining. The basic Particle Swarm Optimization algorithm (PSO) can be combined with the traditional clustering algorithm to achieve clustering analysis. Aiming at the disadvantages of the basic particle swarm optimization algorithm is easy to fall into local extremum, the search accuracy is not high, and the traditional K-means and FCM clustering algorithm are affected by the initial clustering center. This paper proposes a new particle swarm clustering algorithm based on tree structure and neighborhood (TPSO), which designs the structure of the particle group as a tree structure, uses the breadth of traversal, increases the global search ability of the particle, and joins the neighborhood operation to let the particle close to the neighborhood optimal particles and accelerate the convergence speed of the algorithm. Our experiments using Iris, Wine, Seed, Breast-w4 group of UCI public data sets show that the accuracy obtained by the TPSO algorithm implementing the proposed K-means and FCM is statistically significantly higher than the accuracy of the other clustering algorithms, such as K-means algorithm, fuzzy C-means algorithm, the basic particle swarm optimization combined with traditional clustering algorithm, etc., Comparison experiments also indicate that the TPSO algorithm can significantly improve the clustering performance of PSO.