A Novel Hybrid High-Dimensional PSO Clustering Algorithm Based on the Cloud Model and Entropy

With the increase in number of high-dimensional data, characteristic phenomenon unbalanced distribution is increasingly presented various big data applications. At same time, most existing clustering and feature selection algorithms are based on maximizing accuracy. In addition, hybrid approach can effectively solve problem data. Aiming at shortcomings algorithm, a multi-objective PSO algorithm proposed cloud model entropy (HHCE-MOPSO). Furthermore, feasibility verified by simulation test function. The results not only broaden new theory method for but also verify accuracy PSO. analysis information method. As result, research have both important scientific value good practical significance.