Constructing an Enterprise Management System Using an Improved Clustering Algorithm

Abstract

Cluster analysis is one of the most commonly used data analysis methods, and K-means is among the most popular and widely used clustering algorithms in recent years. Because of its performance in clustering huge amounts of enterprise data, the K-means algorithm is one of the most commonly used clustering methods in enterprises. Furthermore, in the context of the new era, enterprises are under growing pressure from both domestic and international sources, particularly market competition pressure. Moreover, the traditional K-means dispersed clustering algorithm has several issues when it comes to clustering big data in enterprises, such as instable clustering findings, poor clustering results, and low execution effectiveness. Based on the premise of the clustering algorithm in enterprises, this paper constructs an improved K-means clustering algorithm of the enterprise management system. It enables businesses to quickly address deficiencies, improve information in all areas, supplement adequate resources, and promote growth. The proposed algorithm uses the sample density, the distance between the clusters, and the cluster compact density, and defines the product of the three as different weight densities and the maximum difference as the initial cluster center to solve the problem. It finds the point of sampling with weight density, randomness, and low quality of early cluster center selection. The experimental results show that my planned algorithm has higher execution efficiency, accurateness, a lower error rate, and good stability.