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国外计算机科学经典教材·数据挖掘:概念、模型、方法和算法(第2版)

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《国外计算机科学经典教材·数据挖掘:概念、模型、方法和算法(第2版)》特色:
介绍支持向量机(SVM)和Kohonen映射
讲解DBSCAN、BIRCH和分布式DBSCAN聚类算法
介绍贝叶斯网络,讨论图形中的Betweeness和Centrality参数测量算法
分析在建立决策树时使用的CART算法和基尼指数
介绍Bagging&Boosting集成学习方法,并详述AdaBoost算法
讨论Relief以及PageRank算法
讨论文本挖掘的潜在语义分析(LSA),并分析如何测定文本文档之间的语义相似性
讲解时态、空间、Web、文本、并行和分布式数据挖掘等新主题
更详细地讲解数据挖掘技术商业、隐私、安全和法律方面的内容
Content Description

随着数据集规模和复杂度的持续上升,分析员必须利用更高级的软件工具来执行间接的、自动的智能化数据分析。、数据挖掘:概念、模型、方法和算法(第2版)介绍了通过分析高维数据空间中的海量原始数据来提取用于决策的新信息的尖端技术和方法。
《国外计算机科学经典教材·数据挖掘:概念、模型、方法和算法(第2版)》开篇阐述数据挖掘原理,此后在示例的引导下详细讲解起源于统计学、机器学习、神经网络、模糊逻辑和演化计算等学科的具有代表性的、最前沿的挖掘方法和算法。本书还着重描述如何恰当地选择方法和数据分析软件并合理地调整参数。每章末尾附有复习题。
《国外计算机科学经典教材·数据挖掘:概念、模型、方法和算法(第2版)》主要用作计算机科学、计算机工程和计算机信息系统专业的研究生数据挖掘教材,高年级本科生或具备同等教育背景的读者也完全可以理解本书的所有主题。
Author Description

Mehmed Kantardzic,美国路易维尔大学工学院计算机工程与科学系的教授,任该系的研究生院院长和数据挖掘实验室主任。Mehmed是IEEE、ISCA和SPIE的成员,曾受邀参加多个重要的学术会议;他已出版多本书籍,在许多期刊上发表过论文,其中有多篇论文获奖。

王晓海,曾任总参某部应用研发中心副主任、信息服务中心主任,高级工程师,长期从事数据库应用系统的科研开发工作,负责主持多个大型数据库系统的开发和维护,荣获多项军队科技进步奖,享受军队优秀人才岗位津贴,出版多部论(译)著,在数据库挖掘、数据库应用开发、数据安全保护、数据恢复与数据去密等领域具有丰富的实践经验。
已出版的论著和译著
《Oracle Streams llg数据复制》,2012年,清华大学出版社
《SQL Server 2000管理、开发及应用实例详解》,2006年,人民邮电出版社
《空时编码技术》,2004年,机械工业出版社
《远程通信网络基础》,1996年,电子工业出版社

吴志刚,工学博士,北京邮电大学副教授,长期从事网络与信息安全技术、数据库技术等领域的学术与科研工作,作为负责人主持过上述领域多项国家863计划、发改委产业化示范项目和国家级重大工程项目,获得技术专利2项,已在国内外学术期刊和国际会议上发表20余篇学术论文。
Catalogue

第1章 数据挖掘的概念
1.1 概述
1.2 数据挖掘的起源
1.3 数据挖掘过程
1.4 大型数据集
1.5 数据仓库
1.6 数据挖掘的商业方面:为什么数据挖掘项目会失败
1.7 本书结构安排
1.8 复习题
1.9 参考书目

第2章 数据准备
2.1 原始数据的表述
2.2 原始数据的特性
2.3 原始数据的转换
2.3.1 标准化
2.3.2 数据平整
2.3.3 差值和比率
2.4 丢失数据
2.5 时间相关数据
2.6 异常点分析
2.7 复习题
2.8 参考书目

第3章 数据归约
3.1 大型数据集的维度
3.2 特征归约
3.2.1 特征选择
3 .2.2 特征提取
3.3 Relief算法
3.4 特征排列的熵度量
3.5 主成分分析
3.6 值归约
3.7 特征离散化ChiMerge技术
3.8 案例归约
3.9 复习题
3.10 参考书目

第4章 从数据中学习
4.1 学习机器
4.2 统计学习原理
4.3 学习方法的类型
4.4 常见的学习任务
4.5 支持向量机
4.6 kNN:最近邻分类器
4.7 模型选择与泛化
4.8 模型的评估
4.9 90%准确的情形
4.9.1 保险欺诈检测
4.9.2 改进心脏护理
4.10 复习题
4.11 参考书目

第5章 统计方法
5.1 统计推断
5.2 评测数据集的差异
5.3 贝叶斯定理
5.4 预测回归
5.5 方差分析
5.6 对数回归
5.7 对数-线性模型
5.8 线性判别分析
5.9 复习题
5.10 参考书目

第6章 决策树和决策规则
6.1 决策树
6.2 C4.5算法:生成决策树
6.3 未知属性值
6.4 修剪决策树
6.5 C4.5算法:生成决策规则
6.6 CART算法和Gini指标
6.7 决策树和决策规则的局限性
6.8 复习题
6.9 参考书目

第7章 人工神经网络
7.1 人工神经元的模型
7.2 人工神经网络的结构
7.3 学习过程
7.4 使用ANN完成的学习任务
7.4.1 模式联想
7.4.2 模式识别
7.5 多层感知机
7.6 竞争网络和竞争学习
7.7 SoM
7.8 复习题
7.9 参考书目

第8章 集成学习
8.1 集成学习方法论
8.2 多学习器组合方案
8.3 bagging和boosting
8.4 AdaBoost算法
8.5 复习题
8.6 参考书目

第9章 聚类分析
9.1 聚类的概念
9.2 相似度的度量
9.3 凝聚层次聚类
9.4 分区聚类
9.5 增量聚类
9.6 DBSCAN箅法
9.7 BIRCH算法
9.8 聚类验证
9.9 复习题
9.10 参考书目

第10章 关联规则
10.1 购物篮分析
10.2 Apriori算法
10.3 从频繁项集中得到关联规则
10.4 提高Apriori算法的效率
10.5 FP增长方法
10.6 关联分类方法
10.7 多维关联规则挖掘
10.8 复习题
10.9 参考书目

第11章 Web挖掘和文本挖掘
11.1Web挖掘
11.2 Web内容、结构与使用挖掘
11.3 HITS和LOGSOM算法
11.4 挖掘路径遍历模式
11.5 PageRank算法
11.6 文本挖掘
11.7 潜在语义分析
11.8 复习题
11.9 参考书目

第12章 数据挖掘高级技术
12.1 图挖掘
……
第13章 遗传算法
第14章 模糊集和模糊逻辑
第15章 可视化方法
附录A 数据挖掘工具
附录B 数据挖掘应用
Book Abstract

4.9.2 改进心脏护理
在美国,心血管疾病每年导致大约100万人死亡(占所有死亡病例的38%)。此外,2005年CVD估计开销为3940亿美元,而癌症所花费的开销大约1900亿美元。CVD患者数量快速发展,直接受此疾病影响的人口数量不断增加。当然需要更好地了解该疾病的情况。目前已经有一些由专家组提出的治疗CVD患者的指导建议。由于当前医疗系统的负担问题,医生对每个患者仅能花费很少的时间。尽管目前存在大量指导建议,但不能指望医生能够针对每个患者按照指导建议制定方案。所以希望能够建立一个系统来辅助医生给出建议方案而不会增加额外的医疗开销。
本研究案例概述了一个称为REMIND系统的使用和部署情况,该系统发现需要系统中的紧急患者,能够更好地跟踪病人按照指导建议进行治疗。目前对每个患者存在两类主要的记录,财务记录和临床记录。财务记录用于付账。这些记录使用标准代码(例如,ICD-9)用于医生评价和药品处方。标准化有助于计算机系统能够直接获取这些记录的信息并方便地应用于数据挖掘过程。然而,由于各种原因,实际发现这些代码仅在60%-80%的情况下是准确的。原因之一是当在付账时使用这些代码时,尽管症状和药房这两个条件基本相同,但保险所支付的价格却存在差异。另外一种记录是临床记录。临床记录由无结构的文本信息构成。允许医生之间传输患者的情况和治疗方案。这些记录更准确,但是由自动化的计算机系统来使用却不太方便。
让医生和护士花费大量时间输入系统特别需要的其他数据是不可能的。为此,REMIND系统采用的方法是合并从各种可用的系统中获取的数据。包括从无结构的临床记录中获取的知识。REMIND系统获取所有当前可用的数据源,利用数据冗余获得患者的状态。例如,为确定患者是否有糖尿病,可使用下列数据:糖尿病账单代码250.xx,诊断糖尿病的文本描述,血糖值>300,使用胰岛素或口服降糖药治疗方案,或常见的糖尿病并发症等。随着相关信息不断获取,患者患有糖尿病的可能性不断增加。REMIND系统从所有可用的数据源获取信息,并在贝叶斯网络中合并。网络的各种输出与各种临时信息可用于发现预先定义的马尔可夫疾病发展模型的最可能状态顺序。贝叶斯网络的概率和结构利用了预先由专家提供的领域知识并且其部署是可调整的。最初由设计者说明的REMIND系设置和临时设置使系统执行高效。然而,在REMIND系统被大量分发前需要进行细致的参数调整工作。
……

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