可调多趟聚类挖掘在电信数据分析中的应用

电信业务每天都产生大量数据，如何从这些数据中提取有用的信息是当今数据挖掘的难题之一。针对实际应用中存在聚类簇数难以确定、单趟聚类算法有时不能收敛到用户指定的簇数等问题，提出了可调多趟聚类挖掘方法。第1趟通过引入一个较大的K值，采用K-means聚类算法，获得K个簇，为第2趟聚类的簇数及簇中心初始值选择提供参考。经电信现网业务数据实验，本文的方法既改善了原聚类方法的局部收敛性，又能较好地适应用户的不同数据分析需求，该方法可用于不确定簇数的大数据分析中。

The Application of the Adjustable Multi-times Clustering Algorithm in Telecom Data

Huge amounts of telecom data are generated every day , so how to extract useful information from the data is one of the data mining problems .Because different applications need different clusters , sometimes a single K-means cluster algorithm cannot generate user-specified K clusters.An adjustable multi-times clustering mining method is proposed .A big value K was used in the K-means clustering al-gorithm for the first time , and K clusters were obtained .They were used to select the number of the clus-ters and the initial centers of the clusters for the second time .The experimental results show that our method is effective , and it can be applied to mining different amounts of clusters and big data analysis .