# A Novel Hybrid High-Dimensional PSO Clustering Algorithm Based on the Cloud Model and Entropy
## 5 Relevant Conclusions  
A novel, hybrid, high-dimensional PSO clustering algorithm based on the cloud model and entropy theory is proposed by applying the cloud model and entropy theory to analyze the relationship between similarity measurement and data distribution. It can effectively eliminate the unimportant features in high-dimensional data sets, thus improving the performance of the hybrid clustering algorithm. The extensive experimental results on some classical benchmark functions demonstrate that the proposed hybrid algorithm produces encouraging results compared with the clustering techniques established in the literature. With the content, which is from general to complex and from unconstrained problems to constrained problems, we propose different HHCE-MOPSO algorithms that achieve a promising result by combining the excellent features, which was developed by hybridization of the PSO in this work. Through in-depth analysis, we found that the clustering algorithm is prone to premature or stagnation problems in the optimization process, which is mainly due to the insufficient speed updating ability of each particle in the late stage of PSO. In the meantime, we use the cloud model theory to realize the dynamic adjustment of the multi-rule uncertainty of the inertia weight, and the test results of test function show that this method has a fast convergence speed and a good optimization effect. Combined with frontier group intelligent optimization, the scope of multi-objective optimization problem is expanded. Subsequently, the obtained experimental results demonstrate that the hybrid algorithm is suitable for both scientific research and engineering applications, although there are also some shortcomings inevitably. Therefore, it is necessary to improve the proposed algorithm according to the characteristics of actual problems, so that it can better solve engineering problems with certain characteristics. In summary, we have presented the HHCE-MOPSO algorithm. Through analysis, we found that it is a new idea to design a clustering algorithm to improve clustering quality and speed effectively. In addition, we also proved that the updating mechanism and the parameter design of the population mechanism of the multi-objective particle swarm optimization clustering algorithm are critical to obtain the optimal solution through a series of experiments. At the same time, we found that the similarity between objects is measured by information entropy and the similarity threshold is directly calculated by the data. In addition, information entropy clustering can be used to merge similar factors and can simplify the complexity of the system. Furthermore, a series of experiments showed that the HHCE-MOPSO algorithm, which takes advantages of the PSO, can achieve better clustering results and is feasible and effective. Hence, sustainability and dynamicity of  
Figure 48: The simulationclustering results with HHCE-MOPSO. big data have been augmented by extending the HHCE-MOPSO algorithm to provide automatic clustering of the data. However, the data presents the characteristics of high dimensions and small samples with the advent of the era of big data. Therefore, we will need to further study a new feature selection clustering method suitable for high-dimensional and small sample imbalanced data. Although we have made some research achievements, unbalanced data clustering is a complex frontier problem. In the future, we should design an unbalanced clustering algorithm suitable for high-dimensional and small samples and improve the clustering effect and clustering performance by the spatial information. Conceptualization, R.-L.Z. and X.-H.L. ; methodology, R.-L.Z. ; software, R.-L.Z. ; validation, R.-L.Z. and X.-H.L. ; writing--original draft preparation, X.-H.L. ; writing--review and editing, X.-H.L. ; project administration, X.-H.L. ; funding acquisition, R.-L.Z. and X.-H.L. All authors have read and agreed to the published version of the manuscript.