The Application of Elastic Network Algorithm Based on Weighted Property in Clustering Analysis

Abstract

With the rapid development of internet technology, the internet has provided convenience for people's lives, and people's lives and work are increasingly inseparable from the internet. However, the various types of information data in the Internet are very large, and accurately obtaining useful information from them has become particularly important. Therefore, clustering analysis technology is widely applied in various fields. The elastic network algorithm is vulnerable to noise and other factors in clustering analysis, resulting in low accuracy of clustering results. To solve this problem, this study proposes an elastic network algorithm based on weighted properties for clustering analysis. After comparative experiments, the results show that the accuracy of the proposed algorithm is 98%; The area under the precision recall curve is 0.86; The maximum F-value is 0.93; The area under the working characteristic curve of the subject is 0.94; The precision ultimately stabilized at 97%; All values are higher than the comparison algorithm. In the experiment, the running time of the algorithm was 11 seconds and the average error was 7%, both of which were lower than the comparison algorithm. When the algorithm is affected by Statistical dispersion and threshold parameters, the accuracy of the proposed algorithm is 71%, 69% and 67% respectively, which are better than the comparison algorithm. In conclusion, the elastic network algorithm based on the weighted characteristics proposed in the study has high accuracy, strong stability and advantages in clustering.