移动通信大数据分析：数据挖掘与机器学习实战

第1章概述
1.1 电信业大数据分析 ···························1
1.2 电信大数据分析的驱动力 ················2
1.3 大数据分析对电信产业价值链的
益处 ··················································3
1.4 电信大数据的实现范围····················4
1.4.1 网络分析 ···················································5
1.4.2 用户与市场分析 ·······································8
1.4.3 创新的商业模式 ·······································91.5 本书概要 ··········································9
参考文献 ·················································10
第2章电信分析方法论
2.1 回归方法 ········································12
2.1.1 线性回归 ··················································13
2.1.2 非线性回归 ··············································15
2.1.3 特征选择 ··················································16
2.2 分类方法 ········································18
2.2.1 逻辑回归 ··················································18
2.2.2 其他分类方法 ··········································19
2.3 聚类方法 ········································20
2.3.1 K均值聚类 ··············································21
2.3.2 高斯混合模型 ··········································23
2.3.3 其他聚类方法 ··········································24
2.3.4 聚类方法在电信数据中的应用 ·················25
2.4 预测方法 ········································25
2.4.1 时间序列分解 ··········································26
2.4.2 指数平滑模型 ··········································27
2.4.3 ARIMA模型 ············································28
2.5 神经网络和深度学习 ·····················29
2.5.1 神经网络 ··················································29
2.5.2 深度学习 ··················································31
2.6 强化学习 ········································32
2.6.1 模型和策略 ··············································33
2.6.2 强化学习算法 ··········································33
参考文献 ·················································34

XII
XII

第3章 LTE网络性能趋势分析
3.1 网络性能预测策略 ·························39
3.1.1 直接预测策略 ··········································39
3.1.2 分析模型 ··················································39
3.2 网络资源与性能指标之间的关系 ···40
3.2.1 LTE网络KPI与资源之间的关系 ···········40
3.2.2 回归模型 ··················································41
3.3 网络资源预测 ·································43
3.3.1 LTE网络流量与资源预测模型 ···············43
3.3.2 预测网络资源 ··········································43
3.4 评估RRC连接建立的应用 ············46
3.4.1 数据准备与特征选取 ······························46
3.4.2 LTE KPI与网络资源之间的关系推导 ····47
3.4.3 预测RRC连接建立成功率 ·····················49
参考文献 ·················································50
第4章热门设备就绪和返修率分析
4.1 设备返修率与设备就绪的预测
策略 ················································53
4.2 设备返修率和就绪预测模型 ··········54
4.2.1 预测模型的移动通信服务 ························54
4.2.2 参数获取与存储 ······································55
4.2.3 分析引擎 ··················································56
4.3 实现和结果 ·····································58
4.3.1 设备返修率预测 ······································58
4.3.2 设备就绪预测 ··········································62
第5章 VoLTE语音质量评估
5.1 应用POLQA评估语音质量··········68
5.1.1 POLQA标准···········································68
5.1.2 语音质量评价中的可扩展性和
可诊断性 ··················································69
5.2 CrowdMi方法论 ····························69
5.2.1 基于RF特征的分类 ·······························70
5.2.2 网络指标选择与聚类 ······························70
5.2.3 网络指标与POLQA评分之间的关系····70
5.2.4 模型测试 ··················································70
5.3 CrowdMi中的技术细节 ·················71
5.3.1 记录分类 ··················································71
5.3.2 网络指标的选择 ······································71
5.3.3 聚类 ·························································72
5.3.4 回归 ·························································73
5.4 CrowdMi原型设计与试验 ·············74
5.4.1 客户端和服务器架构 ······························74
5.4.2 测试和结果 ··············································76
参考文献 ·················································78

目 录XIII

目 录XIII
第6章移动APP无线资源使用分析
6.1 起因和系统概述 ·····························80
6.1.1 背景和挑战 ··············································80
6.1.2 移动资源管理 ··········································81
6.1.3 系统概述 ··················································82
6.2 AppWiR众包工具 ··························83
6.3 AppWiR挖掘算法 ··························84
6.3.1 网络指标的选择 ······································84
6.3.2 LOESS方法 ············································87
6.3.3 基于时间序列的网络资源使用预测 ·······87
6.4 实现和试验 ·····································88
6.4.1 数据收集与研究 ······································88
6.4.2 结果和准确度 ··········································89
参考文献 ·················································91
第7章电信数据的异常检测
7.1 模型 ················································93
7.1.1 高斯模型 ··················································94
7.1.2 时间依赖的高斯模型 ······························94
7.1.3 高斯混合模型（GMM）·························95
7.1.4 时间依赖的高斯混合模型 ·······················95
7.1.5 高斯概率潜在语义模型（GPLSA）·······95
7.2 模型对比 ········································97
7.2.1 样本定义 ··················································97
7.2.2 异常识别 ··················································98
7.2.3 时间依赖GMM与GPLSA的对比 ·········997.3 仿真与讨论 ···································100
参考文献 ···············································103
第8章基于大数据分析的LTE网络自优化
8.1 SON（自组织网络）···················105
8.2 APP-SON ······································107
8.3 APP-SON架构 ·····························108
8.4 APP-SON算法 ·····························110
8.4.1 匈牙利算法辅助聚类（HAAC）··········111
8.4.2 单位回归辅助聚类数的确定 ·················114
8.4.3 基于DNN的回归·································114
8.4.4 每个小区在时序空间的标签组合 ·········116
8.4.5 基于相似性的参数调整 ·························1168.5 仿真与讨论 ···································117
参考文献 ···············································122
第9章电信数据和市场营销
9.2.1 数据采集和数据类型 ····························130
9.1 电信营销专题 ·······························127
9.2.2 网络的提取和管理 ································131
9.2 社交网络的总体构建 ···················130



9.3 网络结构的度量 ···························133
参考文献 ···············································135

9.4 网络中的消费者行为建模 ············134
第10章传染式客户流失
10.1 问题引入 ·····································138
10.1.1 流失率问题 ··········································138
10.1.2 社交学习和网络效应 ··························139
10.2 网络数据的处理 ·························141
10.3 动态模型 ·····································143
10.3.1 模型介绍 ··············································143
10.3.2 模型的定义 ··········································144
10.3.3 自身经验建模、社交学习和
社交网络效应 ······································146
10.3.4 模型估计 ··············································148
10.4 结果 ············································149
参考文献 ···············································151
第11章基于社交网络的精准营销
11.1 网络效应的渠道 ·························158
11.2 社交网络数据处理 ·····················159
11.3 建模策略问题 ·····························160
11.3.1 线性空间自回归模式 ···························160
11.3.2 社交网络交互模型 ······························162
11.3.3 内生同伴效应 ······································162
11.4 发现与应用 ·································164
11.4.1 结果的解释 ··········································164
11.4.2 基于社交网络的精准营销 ···················165
参考文献 ···············································168
第12章社交影响和动态社交网络结构
12.1 动态模型 ·····································17712.1.1 连续时间马尔可夫模型假设 ···············17712.1.2 模型估计与识别 ··································17912.1.3 网络结构对社交影响的多元分析 ·······18012.2 研究发现总结 ·····························18112.2.1 随机行动者动态网络模型的
估计结果··············································182

12.2.2 元回归分析结果 ··································184
12.2.3 策略模拟 ··············································18812.3 结论 ············································193
参考文献 ···············································194