With the rapid development of information technology, especially network technology, people’s ability to collect, store and transmit data are increasing. The data have exploded in an explosive manner. In sharp contrast, the ability to make valuable data for decision making is very poor. In this paper, data mining is the most basic problem. In order to overcome the shortcomings of the traditional clustering algorithm for k-means clustering, it is difficult to determine the initial clustering center and the k-means algorithm is improved. When determining the initial K-, the convergence factor is improved and the global optimum is achieved, so as to realize the determination of clustering center. By using improved k-means algorithm to approximate the criminal data, the validity of this method is verified.

随着信息技术尤其是网络技术的飞速发展，人们收集，存储和传输数据的能力正在增强。数据爆炸性地爆炸了。与之形成鲜明对比的是，为决策制定有价值的数据的能力非常差。本文中，数据挖掘是最基本的问题。为了克服传统聚类算法在k均值聚类中的缺点，难以确定初始聚类中心，并对k均值算法进行了改进。确定初始K-值时，提高了收敛因子，达到了全局最优，从而实现了聚类中心的确定。通过使用改进的k-means算法近似犯罪数据，验证了该方法的有效性。