# A Novel Hybrid High-Dimensional PSO Clustering Algorithm Based on the Cloud Model and Entropy
###### Abstract  
With the increase in the number of high-dimensional data, the characteristic phenomenon of unbalanced distribution is increasingly presented in various big data applications. At the same time, most of the existing clustering and feature selection algorithms are based on maximizing the clustering accuracy. In addition, the hybrid approach can effectively solve the clustering problem of unbalanced data. Aiming at the shortcomings of the unbalanced data clustering algorithm, a hybrid high-dimensional multi-objective PSO clustering algorithm is proposed based on the cloud model and entropy (HHCE-MOPSO). Furthermore, the feasibility of the hybrid PSO is verified by the simulation of the multi-objective test function. The results not only broaden the new theory and method of clustering algorithm for unbalanced data, but also verify the accuracy and feasibility of the hybrid PSO. Furthermore, the clustering analysis method based on information entropy is a new method. As a result, the research results have both important scientific value and good practical significance.  
unbalanced data; clustering algorithm; high-dimensional PSO algorithm; cloud model; information entropy +
Footnote †: journal: _Sci.  
there are still some problems that need to be solved urgently. Consequently, how to design a reasonable data balance processing method is a very meaningful problem.  
Clustering has very important scientific research value such as being the most important branch of data mining technology. In addition, data imbalance is an urgent problem to be solved in the field of high-dimensional complex data clustering in practical projects [4]. Under the background of highly unbalanced clustering data, some scholars apply generalized clustering bootstrapping to Gaussian quasi likelihood and robust estimation methods [5]. Simultaneously, the critical factor affecting the performance improvement of unbalanced data is the selection of clustering methods. Aiming at the limitations of existing unbalanced data modeling techniques, a hybrid clustering algorithm based on boundary sparse samples is proposed. Eventually, the experimental results show that the algorithm has good performance on different data sets with an average increase of 3.5% [6]. In the meantime, the clustering methods for serious class imbalance data and the clustering performance of these methods will significantly decline with the increase in the imbalance ratio. By extending the nonparametric estimation method, the clustering data can satisfy the similarity of objects in the same cluster. In this way, the objects between different clusters can be reduced [7]. The clustering method is sensitive to the initial solution, and it is easy to fall into the local optimum. Furthermore, a fuzzy c-means clustering algorithm based on optimal selection is proposed [8]. The optimal supervised C-means clustering algorithm discusses the alignment of a supervised orthogonal linear local tangent space [9]. In the meantime, the comparison results show that the local search algorithm is superior to other advanced algorithms in terms of solution quality and running time [10]. Simultaneously, it introduces the high-dimensional particle swarm optimization(PSO) algorithm presented by [PERSON] and [PERSON] that it is a bio-inspired optimization technique based on arandom search to solve some clustering problems. The above research results further expand the application field of high-dimensional, multi-objective optimization problems and improves the ability of multi-objective PSO to solve practical problems such as pattern recognition, data mining, and machine learning. Therefore, the clustering is not only widely used in real life, but also has great theoretical significance.