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大数据时代的R语言·数据挖掘：R语言实战

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大数据时代的R语言·数据挖掘：R语言实战

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数据挖掘技术是当下大数据时代最关键的技术，其应用领域及前景不可估量。R是一款极其优秀的统计分析和数据挖掘软件，《大数据时代的R语言·数据挖掘：R语言实战》侧重使用R进行数据挖掘，重点讲述了R的数据挖掘流程、算法包的使用及相关工具的应用，同时结合大量精选的数据挖掘实例对R软件进行深入潜出和全面的介绍，以便读者能深刻理解R的精髓并能快速、高效和灵活地掌握使用R进行数据挖掘的技巧。
通过《大数据时代的R语言·数据挖掘：R语言实战》，读者不仅能掌握使用R及相关的算法包来快速解决实际问题的方法，而且能得到从实际问题分析入手，到利用R进行求解，以及对挖掘结果进行分析的全面训练。

Author Description

王正林已出版过如下图书：1、MATLAB/Simulink与控制系统仿真， 电子工业出版社2、精通MATLAB7(附光盘) 电子工业出版社3、精通MATLAB7科学计算(附光盘) 电子工业出版社
Catalogue

第0章 致敬，R！
致敬，肩膀！
致敬，时代！
致敬，人才！
致敬，R 瑟！
上篇 数据预处理
第1章 数据挖掘导引
1.1 数据挖掘概述
1.1.1 数据挖掘的过程
1.1.2 数据挖掘的对象
1.1.3 数据挖掘的方法
1.1.4 数据挖掘的应用
1.2 数据挖掘的算法
1.3 数据挖掘的工具
1.3.1 工具的分类
1.3.2 工具的选择
1.3.3 商用的工具
1.3.4 开源的工具
1.4 R 在数据挖掘中的优势
数据挖掘：R 语言实战
VI
第2章 数据概览
2.1 n×m 数据集
2.2 数据的分类
2.2.1 一般的数据分类
2.2.2 R 的数据分类
2.2.3 用R 简单处理数据
2.3 数据抽样及R 实现
2.3.1 简单随机抽样
2.3.2 分层抽样
2.3.3 整群抽样
2.4 训练集与测试集
2.5 本章汇总
第3章 用R 获取数据
3.1 获取内置数据集
3.1.1 datasets 数据集
3.1.2 包的数据集
3.2 获取其他格式的数据
3.2.1 CSV 与TXT 格式
3.2.2 从Excel 直接获取数据
3.2.3 从其他统计软件中获取数据
3.3 获取数据库数据
3.4 获取网页数据
3.5 本章汇总
第4章 探索性数据分析
4.1 数据集
4.2 数字化探索
4.2.1 变量概况
4.2.2 变量详情
4.2.3 分布指标
4.2.4 稀疏性
4.2.5 缺失值
4.2.6 相关性
4.3 可视化探索
4.3.1 直方图
目 录
VII
4.3.2 累积分布图
4.3.3 箱形图
4.3.4 条形图
4.3.5 点阵图
4.3.6 饼图
4.5 本章汇总
第5章 数据预处理
5.1 数据集加载
5.2 数据清理
5.2.1 缺失值处理
5.2.2 噪声数据处理
5.2.3 数据不一致的处理
5.3 数据集成
5.4 数据变换
5.5 数据归约
5.6 本章汇总
中篇 基本算法及应用
第6章 关联分析
6.1 概述
6.2 R 中的实现
6.2.1 相关软件包
6.2.2 核心函数
6.2.3 数据集
6.3 应用案例
6.3.1 数据初探
6.3.2 对生成规则进行强度控制
6.3.3 一个实际应用
6.3.4 改变输出结果形式
6.3.5 关联规则的可视化
6.4 本章汇总
第7章 聚类分析
7.1 概述
7.1.1 K-均值聚类
数据挖掘：R 语言实战
VIII
7.1.2 K-中心点聚类
7.1.3 系谱聚类
7.1.4 密度聚类
7.1.5 期望最大化聚类
7.2 R 中的实现
7.2.1 相关软件包
7.2.2 核心函数
7.2.3 数据集
7.3 应用案例
7.3.1 K-均值聚类
7.3.2 K-中心点聚类
7.3.3 系谱聚类
7.3.4 密度聚类
7.3.5 期望最大化聚类
7.4 本章汇总
第8章 判别分析
8.1 概述
8.1.1 费希尔判别
8.1.2 贝叶斯判别
8.1.3 距离判别
8.2 R 中的实现
8.2.1 相关软件包
8.2.2 核心函数
8.2.3 数据集
8.3 应用案例
8.3.1 线性判别分析
8.3.2 朴素贝叶斯分类
8.3.3 K 最近邻
8.3.4 有权重的K 最近邻算法
8.4 推荐系统综合实例
8.4.1 kNN 与推荐
8.4.2 MovieLens 数据集说明
8.4.3 综合运用
8.5 本章汇总
目 录
IX
第9章 决策树
9.1 概述
9.1.1 树形结构
9.1.2 树的构建
9.1.3 常用算法
9.2 R 中的实现
9.2.1 相关软件包
9.2.2 核心函数
9.2.3 数据集
9.3 应用案例
9.3.1 CART 应用
9.3.2 C4.5 应用
9.4 本章汇总
下篇 高级算法及应用
第10章 集成学习
10.1 概述
10.1.1 一个概率论小计算
10.1.2 Bagging 算法
10.1.3 AdaBoost 算法
10.2 R 中的实现
10.2.1 相关软件包
10.2.2 核心函数
10.2.3 数据集
10.3 应用案例
10.3.1 Bagging 算法
10.3.2 Adaboost 算法
10.4 本章汇总
第11章 随机森林
11.1 概述
11.1.1 基本原理
11.1.2 重要参数
11.2 R 中的实现
11.2.1 相关软件包
数据挖掘：R 语言实战
X
11.2.2 核心函数
11.2.3 可视化分析
11.3 应用案例
11.3.1 数据处理
11.3.2 建立模型
11.3.3 结果分析
11.3.4 自变量的重要程度
11.3.5 优化建模
11.4 本章汇总
第12章 支持向量机
12.1 概述
12.1.1 结构风险最小原理
12.1.2 函数间隔与几何间隔
12.1.3 核函数
12.2 R 中的实现
12.2.1 相关软件包
12.2.2 核心函数
12.2.3 数据集
12.3 应用案例
12.3.1 数据初探
12.3.2 建立模型
12.3.3 结果分析
12.3.4 预测判别
12.3.5 综合建模
12.3.6 可视化分析
12.3.7 优化建模
12.4 本章汇总
第13章 神经网络
13.1 概述
13.2 R 中的实现
13.2.1 相关软件包
13.2.2 核心函数
13.3 应用案例
13.3.1 数据初探
目 录
XI
13.3.2 数据处理
13.3.3 建立模型
13.3.4 结果分析
13.3.5 预测判别
13.3.6 模型差异分析
13.3.7 优化建模
13.4 本章汇总
第14章 模型评估与选择
14.1 评估过程概述
14.2 安装Rattle 包
14.3 Rattle 功能简介
14.3.1 Data――选取数据
14.3.2 Explore――数据探究
14.3.3 Test――数据相关检验
14.3.4 Transform――数据预处理
14.3.5 Cluster――数据聚类
14.3.6 Model――模型评估
14.3.7 Evaluate――模型评估
14.3.8 Log――模型评估记录
14.4 模型评估相关概念
14.4.1 误判率
14.4.2 正确/错误的肯定判断、正确/错误的否定判断
14.4.3 精确度、敏感度及特异性
14.5 Rattle 在模型评估中的应用
14.5.1 混淆矩阵
14.5.2 风险图
14.5.3 ROC 图及相关图表
14.5.4 模型得分数据集
14.6 综合实例
14.6.1 数据介绍
14.6.2 模型建立
14.6.3 模型结果分析
数据挖掘：R 语言实战

Introduction

在大数据时代，数据挖掘无疑将是最炙手可热的技术。数据挖掘的理论和方法正曰新月异地发展，数据挖掘的技术及工具，已经滲透到互联网、金融、电商、管理、生产、决策等各个领域，数据挖掘的软件也是层出不穷，其中R是最引入关注的软件。
R是一个免费的开源软件，它提供了首屈一指的统计计算和绘图功能，尤其是大量的数据挖掘方面的算法包，使得它成为一款优秀的、不可多得的数据挖掘工具软件。
本书的主要目的是向读者介绍如何用R进行数据挖掘，通过大量的精选实例，循序渐进、全面系统地讲述R在数据挖掘领域的应用。
本书以数据预处理、基本算法及应用和高级算法及应用这三篇展开。
（1）上篇：数据预处理’
由第1—5章组成，首先简要介绍数据挖掘流程、算法和工具，然后介绍R中的数据分类和数据集，以及使用R获取数据的多种灵活的方法。最后讲述对数据进行探索性分析和预处理的方法。这些内容是使用R进行数据挖掘的最基础内容。
（2）中篇：基本算法及应用
由第6—9章组成，主要讲述数据挖掘的基本算法及应用，包括关联分析、聚类分析、判别分析和决策树，这些算法也是数据挖掘使用最多最普遍的算法。R中提供了丰富的、功能强大的算法包和实现函数，数据挖掘的初级和中级用户务必掌握。
（3）下篇：高级算法及应用
由第10—14章组成，主要讲述数据挖掘的高级算法及应用，包括集成学习、随机森林、支持向量机和神经网络，以及使用R中的工具对数据挖掘的模型进行评估与选择。对于中高级的用户，可以深入学习一下本篇的内容。
R的特点是入门非常容易，使用也非常简单，因此本书不需要读者具备R和数据挖掘的基础知识。不管是R初学者，还是熟练的R用户都能从书中找到对自己有用的内容，快速入门和提高。读者既可以把本书作为学习如何应用R的一本优秀的教材，也可以作为数据挖掘的工具书。
全书以实际问题、解决方案和对解决方案的讨论为主线来组织内容，脉络清晰，并且各章自成体系。读者可以从头至尾逐章学习，也可以根据自己的需要进行学习，根据自己在实际中遇到的问题寻找解决方案。
本书所编写的源程序，都通过了反复调试，方便读者使用。
本书主要由黄文、王正林编写，其他参与编写的人员有付东旭、王思琪、钟太平、刘拥军、陈菜枚、李灿辉、钟事沅、王晓丽、王龙跃、夏路生、钟颂飞、钟杜清、王殿祜等。在此对所有参与编写的人员表示感谢！对关心、支持我们的读者表示感谢！
由于时间仓促，作者水平和经验有限，书中错漏之处在所难免，敬请读者指正。
编者
2014年4月18日于北京

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