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公安大数据应用基础（第2版）

作译者：邱明月,陈俊雹

出版时间：2025-07 千字数：461 版次：01-01 页数：288

开本：16开装帧： I S B N ：9787121507359

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作者简介

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大数据理论模块
第 1 章 大数据与人工智能技术理论 ····························································································2 
1.1 大数据的基本概念 ············································································································2 
1.1.1 大数据的定义·············································································································2
1.1.2 大数据的本质·············································································································2
1.1.3 大数据的分类·············································································································4
1.1.4 大数据的特征·············································································································5
1.1.5 大数据的功能·············································································································6
1.1.6 大数据处理流程 ·········································································································6
1.1.7 大数据处理范式 ·········································································································7
1.2 大数据的技术演进 ············································································································7 
1.2.1 大数据的发展现状······································································································7
1.2.2 大数据的发展趋势······································································································8
1.2.3 人工智能与大模型······································································································9
1.3 大数据的应用··················································································································10 
1.3.1 企业内部大数据 ·······································································································10
1.3.2 在线社交网络大数据·································································································11
1.3.3 健康医疗大数据 ·······································································································11
1.3.4 金融大数据··············································································································12
1.3.5 公安大数据··············································································································12
1.4 常用的数据挖掘工具 ······································································································13 
1.4.1 Tableau····················································································································13
1.4.2 Excel ·······················································································································14
1.4.3 SPSS Modeler ···········································································································14
1.4.4 Python ·····················································································································14
数据分析与挖掘模块
第 2 章 SPSS Modeler 软件·······································································································16
2.1 SPSS Modeler 软件概述·································································································16 
2.1.1 SPSS Modeler 界面····································································································16
2.1.2 数据流的基本管理和执行··························································································18
2.1.3 数据流的其他管理····································································································20
2.1.4 SPSS Modeler 应用案例·····························································································22 
2.2 SPSS Modeler 数据的读入······························································································27 
2.2.1 变量的类型··············································································································27
2.2.2 读数据·····················································································································28
2.2.3 生成实验方案数据····································································································34
2.2.4 数据合并 ·················································································································36
2.3 SPSS Modeler 数据的基本分析 ······················································································40 
2.3.1 数据质量 ·················································································································40
2.3.2 基本描述分析···········································································································46
2.3.3 变量分布探索···········································································································49
2.3.4 二分类型相关性研究·································································································51
2.3.5 两总体的平均值比较·································································································58
2.3.6 变量的重要性分析····································································································64
第 3 章 数据清洗··························································································································69
3.1 数据清洗概述··················································································································69 
3.1.1 数据清洗的概念 ·······································································································69
3.1.2 数据清洗的对象 ·······································································································69
3.1.3 数据清洗的一般步骤·································································································70
3.1.4 数据清洗的常用方式·································································································71
3.1.5 数据清洗的基本方法·································································································71
3.2 Excel 数据清洗的基本操作 ····························································································72 
3.2.1 重复值的处理···········································································································72
3.2.2 缺失值及异常值的处理 ·····························································································76
3.3 Excel 数据加工的基本操作 ····························································································80 
3.3.1 字段分列 ·················································································································80
3.3.2 字段合并 ·················································································································81
3.3.3 字段匹配 ·················································································································82
3.3.4 数据分组 ·················································································································83
3.4 Excel 数据透视表············································································································83 
3.4.1 数据透视表应用 ·······································································································83
3.4.2 数据透视表的实用技巧 ·····························································································87
第 4 章 时间序列分析··················································································································91
4.1 时间序列··························································································································91 
4.1.1 时间序列概述···········································································································91
4.1.2 时间序列的预测步骤·································································································92
4.2 移动平均法······················································································································92 
4.2.1 一次移动平均法 ·······································································································93
4.2.2 二次移动平均法 ·······································································································96
4.3 指数平滑法······················································································································98 
4.3.1 一次指数平滑法 ·······································································································99
4.3.2 二次指数平滑法 ·····································································································100
4.3.3 三次指数平滑法 ·····································································································106
第 5 章 分类预测：决策树········································································································112
5.1 决策树概述····················································································································112 
5.1.1 什么是决策树·········································································································112
5.1.2 决策树的几何理解··································································································113
5.1.3 决策树的核心问题··································································································113
5.2 SPSS Modeler 中的 C5.0 算法及应用···········································································115 
5.2.1 C5.0 决策树的分割点······························································································115
5.2.2 C5.0 决策树的剪枝过程···························································································116
5.2.3 C5.0 决策树的推理规则集 ·······················································································117
5.2.4 C5.0 决策树的应用 ·································································································118
5.3 SPSS Modeler 中的 C＆RT 算法及应用········································································125 
5.3.1 C＆RT 的生长过程··································································································126
5.3.2 C＆RT 的剪枝过程··································································································127
5.3.3 C＆RT 的应用·········································································································129
5.4 SPSS Modeler 中的 CHAID 算法及应用······································································131 
5.4.1 CHAID 算法的最佳分组变量 ···················································································132
5.4.2 CHAID 算法的剪枝过程··························································································132
5.4.3 Exhaustive CHAID 算法···························································································133
5.4.4 CHAID 算法的应用·································································································133
5.5 SPSS Modeler 中的 QUEST 算法及应用······································································134 
5.5.1 QUEST 算法的最佳分割点 ······················································································134
5.5.2 QUEST 算法的应用 ································································································135
5.6 决策树算法的评估和注意事项·····················································································136 
实验 运用 4 种决策树算法预测数据··················································································137 
第 6 章 分类预测：人工神经网络 ····························································································145
6.1 人工神经网络概述 ········································································································145 
6.1.1 人工神经网络的概念和种类·····················································································145
6.1.2 人工神经网络中的节点 ···························································································147
6.1.3 建立人工神经网络的一般步骤 ·················································································149
6.2 SPSS Modeler 中的 B-P 反向传播网络 ········································································151 
6.2.1 感知器模型············································································································151
6.2.2 B-P 反向传播网络···································································································154
6.2.3 B-P 反向传播算法···································································································156
6.2.4 B-P 反向传播网络的建立·························································································158
6.3 SPSS Modeler 中的径向基函数网络 ············································································161 
6.3.1 径向基函数网络 ·····································································································161
6.3.2 径向基函数网络中的隐藏层节点和输出节点 ·····························································162
6.3.3 径向基函数网络的学习过程·····················································································163
6.4 人工神经网络的应用 ····································································································164 
第 7 章 分类预测：Logistic 回归分析······················································································178
7.1 二项 Logistic 回归方程 ·································································································178 
7.1.1 二项 Logistic 回归方程概述 ·····················································································178
7.1.2 二项 Logistic 回归方程中系数的含义········································································180
7.2 二项 Logistic 回归分析的应用 ·····················································································182 
7.3 多项 Logistic 回归分析的应用 ·····················································································186 
第 8 章 关联分析························································································································187
8.1 简单关联规则分析 ········································································································187 
8.1.1 简单关联规则的基本概念························································································188
8.1.2 简单关联规则的有效性和实用性··············································································189
8.2 Apriori 算法···················································································································192 
8.2.1 寻找频繁项集·········································································································192
8.2.2 依据频繁项集产生简单关联规则··············································································194
8.3 Apriori 算法的应用 ·······································································································195 
8.4 序列关联规则分析 ········································································································202 
8.4.1 序列关联规则的基本概念························································································202
8.4.2 序列关联规则的时间约束························································································203
8.5 Sequence 算法················································································································204 
8.5.1 产生频繁序列集 ·····································································································204
8.5.2 依据频繁序列集生成序列关联规则 ··········································································205
8.6 Sequence 算法的应用····································································································206 
第 9 章 聚类分析························································································································210
9.1 聚类分析概述················································································································210 
9.2 K-Means 聚类算法及应用·····························································································211 
9.2.1 K-Means 聚类算法对“亲疏程度”的衡量·································································211
9.2.2 K-Means 聚类过程··································································································211
9.2.3 K-Means 聚类算法的应用························································································213
9.3 两步聚类算法及应用 ····································································································221 
9.3.1 两步聚类算法对“亲疏程度”的衡量 ·······································································221
9.3.2 两步聚类过程·········································································································222
9.3.3 两步聚类算法的应用·······························································································224
9.4 Kohonen 网络聚类算法及应用·····················································································226 
9.4.1 Kohonen 网络聚类算法的原理 ·················································································226
9.4.2 Kohonen 网络聚类过程 ···························································································227
9.4.3 Kohonen 网络聚类算法的应用 ·················································································229
9.5 基于聚类分析的离群值探索及应用·············································································232 
9.5.1 多维空间基于聚类的诊断方法 ·················································································232
9.5.2 多维空间基于聚类的诊断方法的应用 ·······································································234
数据可视化模块
第10章 数据可视化 ·················································································································240
10.1 数据可视化入门··········································································································240 
10.1.1 i2 Analyst’s Notebook 8 软件···················································································240
10.1.2 基本概念··············································································································241
10.1.3 数据接口··············································································································245
10.2 基本功能······················································································································245 
10.2.1 基本操作··············································································································245
10.2.2 搜索查找··············································································································247
10.3 功能演练······················································································································250 
10.3.1 话单关系分析·······································································································250
10.3.2 人员物品动态关系 ································································································257
10.3.3 银行账户交易分析 ································································································261
10.3.4 话单 ABC 分析 ·····································································································268
10.3.5 盗窃案旅业分析····································································································274
10.3.6 人员活动轨迹·······································································································277

大数据备受各界重视，成为就业前景广阔的领域，大数据人才的短缺将严重制约大数据行业的发展。与此同时，保障社会稳定的相关政府部门，对大数据人才的需求也非常旺盛。在公安领域，相关技术紧跟社会发展的步伐不断更新迭代，因此，在大数据时代的公安工作中，不可避免地会使用大数据技术进行侦查和分析。编写本教材的目的是培养能在互联网侦查、公安情报、公安大数据等业务中从事大数据处理、分析、预测和运维工作的复合型、应用型、技术型人才。
本书为国家级一流本科专业(公安情报学)和国家级一流本科专业(网络安全与执法)建设成果，编者常年从事公安情报、公安大数据建模的教学与科研工作。我们知道，掌握基础计算机知识，但不太了解数据挖掘的原理和方法，渴望通过数据挖掘技术解决公安工作中的问题的读者有很多。所以，本书在编写时着重考虑以下 3 个要点：
(1)以公安实战案例为线索，介绍数据清洗、数据挖掘、数据可视化的方法和流程；
(2)避免罗列较多的数学公式，侧重介绍数据挖掘的核心思想和基本原理；
(3)语言通俗易懂，操作过程翔实，图文并茂。
本书的特点是内容模块化、教学目标实用化、教学案例标准化、教学流程图表化，着重体现了以“应用型”为目标的教学特点，集设计性、通俗性、实战性于一体，重点在于提高读者的公安大数据应用能力，使读者轻松掌握所学的知识，达到事半功倍的效果。本书配有PPT、源代码、全部案例文件，读者可登录华信教育资源网(www.hxedu.com.cn)免费下载。
本书适合作为公安类本科院校和高职高专院校大数据相关课程的教材及参考书，也可供相关技术人员参考。
本书由邱明月、陈俊雹任主编，由高宇、朱庆辉、张慧任副主编，由王新猛任主审。本书共 10 章，其中，邱明月、陈俊编写第 1～6 章，设计了本书案例、组织架构，并进行全书统稿；高宇、朱庆辉、张慧编写第 7～10 章，并负责书中模型的测试。
由于编者水平有限，错误之处在所难免，恳请广大读者和专家批评指正。
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