基于A-D模型的K-means算法在通话异常客户挖掘中的应用

周坚; 石永革; 何美斌

doi:10.11959/j.issn.1000-0801.2018021

您当前的位置：

首页 >

文章列表页 >

基于A-D模型的K-means算法在通话异常客户挖掘中的应用

研究与开发 | 更新时间：2024-06-05

- 基于A-D模型的K-means算法在通话异常客户挖掘中的应用
- Application of K-means algorithm based on A-D model in calling abnormal customer mining
- 电信科学 2018年34卷第4期页码：81-89
- 作者机构：
  
  1. 南昌大学信息工程学院，江西南昌 330029
  2. 中国电信股份有限公司江西分公司，江西南昌 330029
- 作者简介：
  
  [ "周坚（1993-），男，南昌大学信息工程学院硕士生，主要研究方向为数据挖掘、计算机网络。" ]
  [ "石永革（1953-），男，南昌大学信息工程学院教授，主要研究方向为计算机网络、信息安全。" ]
  [ "何美斌（1977-），男，中国电信股份有限公司江西分公司高级工程师，主要研究方向为网络信息安全、云计算及大数据分析。" ]
- 基金信息：
- DOI：10.11959/j.issn.1000-0801.2018021
  中图分类号： TP391
- 网络出版日期：2018-04，
  
  纸质出版日期：2018-04-20
- 稿件说明：
移动端阅览
周坚, 石永革, 何美斌. 基于A-D模型的K-means算法在通话异常客户挖掘中的应用[J]. 电信科学, 2018,34(4):81-89.

Jian ZHOU, Yongge SHI, Meibin HE. Application of K-means algorithm based on A-D model in calling abnormal customer mining[J]. Telecommunications science, 2018, 34(4): 81-89.
周坚, 石永革, 何美斌. 基于A-D模型的K-means算法在通话异常客户挖掘中的应用[J]. 电信科学, 2018,34(4):81-89. DOI： 10.11959/j.issn.1000-0801.2018021.

Jian ZHOU, Yongge SHI, Meibin HE. Application of K-means algorithm based on A-D model in calling abnormal customer mining[J]. Telecommunications science, 2018, 34(4): 81-89. DOI： 10.11959/j.issn.1000-0801.2018021.

摘要

为能够利用海量的语音通信记录，高质量地对各种语音通信行为异常的客户（电信诈骗客户、广告客户等）进行聚类分析，设计构建了语音通信异常客户的行为特征模型，并基于该模型提出了一种语音通信行为异常客户的聚类分析算法。首先，通过分析客户的通话记录得出客户通话次数、接通率等通话行为特征，然后将AHP模型与DEMATEL方法融合，构建语音通信行为异常客户的行为特征模型（AHP-DEMATEL模型）；其次，基于该模型提出了一种改进的K-means算法，实现根据语音通信记录对异常客户进行聚类分析。最后，使用真实数据进行了验证分析。结果表明，相较于其他类似算法，本文算法在多类型异常客户综合聚类分析和单类型异常客户聚类分析时，其性能都得到了较大幅度的提高。

Abstract

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.

关键词

Keywords

references

刘霞 . 基于电信通话记录的数据挖掘 [J ] . 计算机与现代化 , 2008 ( 6 ): 36 - 38 .

LIU X . Application of fuzzy-association rule to telecom data mining [J ] . Computer and Modernization , 2008 ( 6 ): 36 - 38 .

王家定 . 基于复杂网络理论和通话记录的客户行为异常识别研究 [D ] . 合肥:中国科学技术大学 , 2013 .

WANG J D . Research on customer behavior anomaly recognition based on complex network theory and call logging [D ] . Hefei:Uni-versity of Science and Technology of China , 2013 .

石瑛 . 面向电信市场营销的数据挖掘技术研究 [D ] . 上海:华东师范大学 , 2004 .

SHI Y . Research on data mining technology for telecom mar-keting [D ] . Shanghai:East China Normal University , 2004 .

赵凯 , 蒋朝惠 . 基于 K-means 算法的电信客户行为特征聚类分析 [J ] . 贵州大学学报:自然科学版 , 2015 , 32 ( 2 ): 86 - 90 .

ZHAO K , JIANG C H . Analysis of telecom user behavior fea-tures clustering based on K-means algorithm [J ] . Journal of Guizhou University(Natural Science) , 2015 , 32 ( 2 ): 86 - 90 .

江哲雅 . 聚类挖掘在电信客户分类中的研究与应用 [D ] . 上海:上海交通大学 , 2013 .

JIANG Z Y . Research and application of clustering mining in telecom customer classification [D ] . Shanghai:Shanghai Jiao Tong University , 2013 .

关云鸿 . 改进 K-均值聚类算法在电信客户分类中的应用 [J ] . 计算机仿真 , 2011 , 28 ( 8 ): 138 - 140 .

GUAN Y H . Application of improved K-means algorithm in telecom customer segmentation [J ] . Computer Simulation , 2011 , 28 ( 8 ): 138 - 140 .

ZUO G C , ZHOU R H , LI Z Q , et al . Improved K-means algorithm and its CRM in telecom customer segmentation application [J ] . Computer Systems ＆ Applications , 2012 , 19 ( 11 ): 155 - 159 .

FAN G , ZHONG D , YAN F , et al . A hybrid fuzzy evaluation method for curtain grouting efficiency assessment based on an AHP method extended by D numbers [J ] . Expert Systems with Applications , 2016 ( 44 ): 289 - 303 .

TZENG G H , CHIANG C H , LI C W . Evaluating intertwined effects in e-learning programs:a novel hybrid MCDM model based on factor analysis and DEMATEL [J ] . Expert Systems with Applications , 2007 , 32 ( 4 ): 1028 - 1044 .

原福永 , 张晓彩 , 罗思标 . 基于信息熵的精确特征赋权K-means聚类算法 [J ] . 计算机应用 , 2011 , 31 ( 6 ): 1675 - 1677 .

YUAN F Y , ZHANG X C , LUO S B . Accurate property weighted K-means clustering algorithm based on information entropy [J ] . Journal of Computer Applications , 2011 , 31 ( 6 ): 1675 - 1677 .

瞿英 , 路亚静 , 刘紫玉 , 等 . 基于 AHP-DEMATEL 法的权重计算方法研究 [J ] . 数学的实践与认识 , 2016 , 46 ( 7 ): 38 - 46 .

QU Y , LU Y J , LIU Z Y , et al . Research on the weight calcula-tion method based on the AHP-DEMATEL method [J ] . Mathe-matics in Practice and Theory , 2016 , 46 ( 7 ): 38 - 46 .

丁晓琴 , 张德生 . 基于 AHP 和 cRITIc 综合赋权的 K-means算法 [J ] . 计算机系统应用 , 2016 , 25 ( 7 ): 182 - 186 .

DING X Y , ZHANG D S . K-means algorithm based on synthet-ic weighting of AHP and CRITIC [J ] . Computer Systems ＆ Ap-plications , 2016 , 25 ( 7 ): 182 - 186 .

王俊凤 , 闫文 . 基于AHP-DEMATEL模型的黑龙江省农民专业合作社内部资金互助的影响元素分析 [J ] . 金融理论与实践 , 2016 ( 7 ): 86 - 90 .

WANG J F , YAN W . Analysis of the influence of mutual funds between farmer cooperatives in Heilongjiang province based on AHP-DEMATEL model [J ] . Financial Theory and Practice , 2016 ( 7 ): 86 - 90 .

HUNG S J . Activity-based divergent supply chain planning for competitive advantage in the risky global environment:a DEMATEL-ANP fuzzy goal programming approach [J ] . Expert Systems with Applications , 2011 , 38 ( 8 ): 9053 - 9062 .

陈光平 , 王文鹏 , 黄俊 . 一种改进初始聚类中心选择的K-means算法 [J ] . 小型微型计算机系统 , 2012 , 33 ( 6 ): 170 - 173 .

CHEN G P , WANG W P , HUANG J . Improved initial clustering center selection method for K-means algorithm [J ] . Journal of Chinese Computer Systems , 2012 , 33 ( 6 ): 170 - 173 .

熊平 , 顾霄 . 基于特征权重最优化的 K-means 聚类算法 [J ] . 微电子学与计算机 , 2014 ( 4 ): 40 - 43 .

XIONG P , GU X . K-means clustering algorithm based on fea-ture weight optimization [J ] . Microelectronics Computer , 2014 ( 4 ): 40 - 43 .

刘春 , 邹海锋 , 向勇 . 大数据环境下电信数据服务能力开放研究 [J ] . 电信科学 , 2014 , 30 ( 3 ): 156 - 161 .

LIU C , ZOU H F , XIANG Y . Research on telecom data service open ability under the environment of big data [J ] . Telecommu-nications Science , 2014 , 30 ( 3 ): 156 - 161 .

韩晶 , 张智江 , 王健全 , 等 . 面向统一运营的电信运营商大数据战略 [J ] . 电信科学 , 2014 , 30 ( 11 ): 154 - 158 .

HAN J , ZHANG Z J , WANG J Q , et al . The uni-fied-operation-oriented big data strategy for telecom operators [J ] . Telecommunications Science , 2014 , 30 ( 11 ): 154 - 158 .

曹旭 , 曹瑞彤 . 基于大数据分析的网络异常检测方法 [J ] . 电信科学 , 2014 , 30 ( 6 ): 152 - 156 .

CAO X , CAO R T . Network anomaly prediction method based on big data [J ] . Telecommunications Science , 2014 , 30 ( 6 ): 152 - 156 .

谷红勋 , 杨珂 . 基于大数据的移动用户行为分析系统与应用案例 [J ] . 电信科学 , 2016 , 32 ( 3 ): 139 - 146 .

GU H X , YANG K . Mobile user behavior analysis system and applications based on big data [J ] . Telecommunications Science , 2016 , 32 ( 3 ): 139 - 146 .

浏览量

477

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

暂无数据