摘要
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.
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 .
出处
《广东工业大学学报》
CAS
2014年第3期1-7,143,共7页
Journal of Guangdong University of Technology
基金
国家自然科学基金资助项目(61104156
61370229)
国家科技支撑计划项目(2013BAH72B01)
教育部重点实验室基金资助项目(110411)
广东省自然科学基金资助项目(10451009001004804
9151009001000007)
广东省科技计划项目(2012B091000173)
广东省教育厅项目(粤教高函〔2013〕113号)
广州市科技计划项目(2012J5100054
2013J4500028)
