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大数据时代，我们需要对各种海量数据进行筛选、清洗、挖掘，在这个过程中，获取有效数据的方式方法和模型算法成为了整个数据挖掘过程的重点，MATLAB作为一个数据挖掘工具，如何正确和准确地使用它成为了重中之重。 针对实际应用数据挖掘技术的要求，本书既介绍了数据挖掘的基础理论和技术，又较为详细地介绍了各种算法以及MATLAB程序。本书共分4篇，分别介绍了数据挖掘的基本概念、技术与算法以及应用实例。期望通过大量的实例分析帮助广大读者掌握数据挖掘技术，并应用于实际的研究中，提高对海量数据信息的处理及挖掘能力。本书针对性和实用性强，具有较高的理论和实用价值。 本书作者就职于部队高校，专攻数据挖掘，并应用于大量实际项目，本书同时得到了国内著名数据挖掘公司的技术支持，很多案例来自实际项目。 本书可作为高等院校计算机工程、信息工程、生物医学工程、化学、环境、经济、管理等学科的研究生、本科生的教材或教学参考书，亦可作为企事业单位管理者、信息分析人员、市场营销人员和研究与开发人员的参考资料。

 

资料目录：

第1章  绪论··············································································································································· 1

1.1 数据挖掘概述··································································································································· 2

1.2 数据挖掘的分类······························································································································· 4

1.3 数据挖掘的过程······························································································································· 5

1.4 数据挖掘的任务······························································································································· 6

1.5 数据挖掘的对象······························································································································· 8

1.5.1 数据库········································································································································· 8

1.5.2 文本············································································································································· 10

1.5.3 图像与视频数据························································································································· 10

1.5.4  Web数据·································································································································· 11

1.6 数据挖掘建模方法··························································································································· 11

1.6.1 业务理解····································································································································· 12

1.6.2 数据理解····································································································································· 13

1.6.3 数据准备····································································································································· 13

1.6.4 建模············································································································································· 14

1.6.5 评估············································································································································· 15

1.6.6 部署············································································································································· 16

1.7 数据挖掘的应用······························································································································· 16

1.7.1 在金融领域的应用····················································································································· 16

1.7.2 在零售业中的应用····················································································································· 17

1.7.3 在电信业的应用························································································································· 18

1.7.4 在管理中的应用························································································································· 19

1.7.5 在化学研究领域中的应用········································································································· 19

1.7.6 在材料研究、生产方面的应用································································································· 20

1.7.7 在机械故障诊断与监测中的应用····························································································· 21

1.7.8 在医疗领域中的应用················································································································· 22

第2章  数据挖掘算法·························································································································· 25

2.1 决策树算法······································································································································· 26

2.1.1 决策树基本算法························································································································· 27

2.1.2 ID3算法····································································································································· 29

2.1.3 C4.5算法···································································································································· 30

2.1.4 CART算法································································································································· 31

2.1.5 决策树的评价标准····················································································································· 32

2.1.6 决策树的剪枝及优化················································································································· 33

2.1.7 基于matlab的决策树分析········································································································ 34

2.2 人工神经网络算法··························································································································· 41

2.2.1 人工神经网络概述····················································································································· 41

2.2.2 人工神经网络的基本模型········································································································· 41

2.2.3 BP神经网络······························································································································· 43

2.2.4 RBF神经网络···························································································································· 45

2.2.5 SOM神经网络··························································································································· 46

2.2.6 反馈型神经网络（Hopfield）··································································································· 47

2.2.7 基于matlab的神经网络方法···································································································· 49

2.3 进化算法··········································································································································· 55

2.3.1 进化算法的基本原理················································································································· 56

2.3.2 基因算法的主要步骤················································································································· 60

2.3.3 基本遗传算法····························································································································· 61

2.3.4 进化规划算法····························································································································· 63

2.3.5 进化策略计算····························································································································· 64

2.3.6 量子遗传算法····························································································································· 68

2.3.7 人工免疫算法····························································································································· 72

2.3.8 基于matlab的进化算法············································································································ 80

2.4 统计分析方法··································································································································· 87

2.4.1 假设检验····································································································································· 87

2.4.2 回归分析····································································································································· 91

2.4.3 二项逻辑（logistic）回归········································································································· 100

2.4.4 方差分析····································································································································· 104

2.4.5 主成分分析································································································································· 107

2.4.6 因子分析····································································································································· 110

2.4.7 基于matlab的统计分析方法···································································································· 113

2.5 贝叶斯网络方法······························································································································· 141

2.5.1 贝叶斯定理、先验和后验········································································································· 142

2.5.2 贝叶斯网络································································································································· 142

2.5.3 贝叶斯网络学习························································································································· 143

2.5.4 主要贝叶斯网络模型················································································································· 145

2.5.5 基于matlab的贝叶斯网络方法································································································ 148

2.6 支持向量机······································································································································· 160

2.6.1 支持向量机概述························································································································· 160

2.6.2 核函数········································································································································· 162

2.6.3 基于matlab的支持向量机方法································································································ 164