Application of K-means algorithm based on A-D model in calling abnormal customer mining - Jian ZHOU; Yongge SHI; Meibin HE (Dianxin kexue)
In order to make use of massive voice communication records and cluster high-quality clients (telecom fraud clients,advertisers) with various kinds of voice communication abnormalities,a behavioral feature model of abnormal voice communication customers was designed and constructed.Based on the model,a clustering analysis algorithm for customers with abnormal voice communication behavior was proposed.First of all,by analyzing the call records of customers,the characteristics of call behaviors was got,such as the number of calls,call rates,and so on.Then AHP-DEMATEL model was constructed by blending the AHP model and DEMATEL method.Secondly,based on the model,an improved K-means algorithm was proposed to cluster the abnormal clients according to the voice communication records.Finally,the real data was used to verify the analysis.The results show that compared with other similar algorithms,the proposed algorithm improves the performance of multi-type abnormal customer comprehensive clustering analysis and single-type abnormal customer clustering analysis greatly.