实战大数据-MATLAB数据挖掘详解与实战
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图文详情
- ISBN:9787302451013
- 装帧:一般胶版纸
- 册数:暂无
- 重量:暂无
- 开本:32开
- 页数:547
- 出版时间:2017-08-01
- 条形码:9787302451013 ; 978-7-302-45101-3
本书特色
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。
内容简介
这是一本真正具备中国特色的数据挖掘手册,各种常规方法一应俱全,作者是高校教授,同时也是若干公司的数据挖掘顾问,难得的理论储备与实践经验都十分深厚,表达能力也很强,也了解痛点。基于这些背景创作的这本书,非常好。
目录
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
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