SOM+K-means两阶段聚类算法及其应用

SOM+K-means Two-phase Clustering Algorithm and Its Application

在众多聚类算法中,K-means和自组织神经网络(SOM)是较为经典的2个。在分析2种算法优缺点的基础上,提出基于SOM的K-means两阶段聚类算法,该算法根据SOM算法自动聚类的优点得到初步聚类数目和各类中心点,以此作为K-means算法的初始输入进一步聚类,从而得到精确的聚类信息。最后,应用该算法对某地区电信家庭客户数据进行分析,结果表明该算法有较好的聚类效果。

K-means and SOM network are two classical algorithms among many clustering ones.A new SOM-based K-means two-phase clustering algorithm is proposed based on the analysis of the advantages and shortcomings of the two algorithms.The quantity of the preliminary clustering and the central point of each cluster were acquired with K-means algorithm,by means of the auto-clustering advantages of SOM algorithm.Taking the results as the initial input of the K-means algorithm to make the further clustering,the accurate clustering results are gained.The data of the telecom family customers in a district is analyzed with the algorithm.The results confirm that the algorithm is better than SOM network and K-means algorithms when they are separately used.