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Jingdong book

大数据挖掘与机器学习：工业4.0时代重塑商业价值

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大数据挖掘与机器学习：工业4.0时代重塑商业价值

Name: 大数据挖掘与机器学习：工业4.0时代重塑商业价值
Brand: Jingdong book
SKU: 3110062341
Price: 45.04 USD
Availability: OutOfStock

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Editer Recommend

在今天的商业环境中，无穷无尽的大数据潮流经常影响着重要的决策流程。为了保持和延续业务盈利，利用大数据的能力势在必行。但是，只是获得数据和具有处理能力并不足以得到有意义的结果。

《大数据挖掘与机器学习》为市场营销主管、商业领袖和技术专家提供一套综合手段，使其能开发出不断产生有效结果并提高利润率的战略和方法，并且能将这些战略和方法贯彻实施下去。在本书中，Jared Dean对大数据分析现状以及日益提升的高性能计算体系结构的趋势进行深入浅出的回顾。《大数据挖掘与机器学习》明确地展示了如何利用大数据分析促进积极改变并且驱动效率。

Jared Dean循序渐进地揭示了如何应用技术创建数据挖掘、机器学习及大数据处理的分析环境。该作者还探索权衡了不同技术选择的结果。《大数据挖掘与机器学习》囊括了能够加以应用从被挖掘的数据中获取信息的算法和方法，并提供了对如何有效地应用这些方法的解释。本书还提供了许多案例研究的说明，给出了许多已经成功应用了新的技术和算法来建立其竞争优势的组织的例子。作者还对预测建模和如何应用这些工具于决策流程进行了讨论。

对任何希望获得数据分析能力的组织，这本重要的书都可以作为理解大数据基本技术和大数据分析的关键指引。现在你可以充满自信地掌控你所在组织的大数据分析，创造出能够一击即中的结果。

Content Description

《大数据挖掘与机器学习：工业4.0时代重塑商业价值》分为3个部分，共17章。第Ⅰ部分“计算环境”，包括第1章到第3章。第Ⅱ部分“将数据转化为商业价值”，包括第4章到第10章。这一部分聚焦于数据挖掘活动中所要用到的方法、算法和路径。第Ⅲ部分“将其全部结合起来的成功案例”包括第11章到第17章。本部分主要描述了作者参与过的成功应用大数据分析优化企业决策、提高企业价值的公司案例。
《大数据挖掘与机器学习：工业4.0时代重塑商业价值》可作为企业管理人员、营销主管、分析人员、IT人员等作为理解大数据、应用大数据为企业创造价值的指引，同时，本书也可供统计学、应用数学及计算机专业学者和研究人员参考学习。

Author Description

Jared Dean（杰瑞德·迪安）是SAS研究院的研发高级总监。他负责SAS全球数据挖掘解决方案的开发。这包括客户互动、新功能开发、技术支持、销售支持和产品集成。在加入SAS之前，Dean是美国人口调查局的数学统计学家。

Catalogue

1 概述
大数据大事年表
为何这个主题现在很重要
大数据是否只是一时的狂热？
在何处应用大数据会产生重大影响？
21第Ⅰ部分计算环境
23第1章硬件
1．1存储器（磁盘）
1．2中央处理器
1．3内存
1．4网络
31第2章分布式系统
2．1数据库计算
2．2文件系统计算
2．3考虑因素
37第3章分析工具
3．1Weka
3．2Java和JVM语音
3．3R语言
3．4Python
3．5SAS
47第Ⅱ部分将数据转化为商业价值
49第4章预测建模
4．1一个建模方法
4．2sEMMA
4．3二元分类法
4．4多层分类法
4．5区间预测
4．6预测模型评估
63第5章一般预测建模技术
5．1RFM
5．2回归
5．3广义线性模型
5．4神经网络
5．5决策树和回归树
5．6支持向量机
5．7贝叶斯网络分类方法
5．8组合方法
117第6章细分
6．1聚类分析
6．2距离测度（指标）
6．3聚类评估
6．4聚类数量
6．5K-means算法
6．6分层聚类法
6．7群特征刻画
129第7章增量响应建模
7．1建立响应模型
7．2评估增量响应
137第8章时间序列数据挖掘
8．1降维
8．2探查模式
8．3时间序列数据挖掘的应用：Nike+Fuelband智能手环
149第9章推荐系统
9．1何为推荐系统？
9．2应用于何处？
9．3如何起作用？
9．4推荐质量评估
9．5推荐系统的应用：SAS 图书馆
161第10章文本分析
10．1信息检索
10．2内容分类
10．3文本挖掘
10．4文本分析应用：让我们来玩《危险边缘》（Jeopardy！）
177第Ⅲ部分将其全都结合起来的成功案例
179第11章基于某大型美国金融服务公司的案例研究
11．1传统市场营销活动流程
11．2高效的营销解决方案
11．3变革的价值主张
187第12章主要卫生保健提供者的案例研究
12．1CAHPS
12．2HEDIS
12．3HOS
12．4IRE
197第13章技术制造商案例研究
13．1发现设备缺陷
13．2如何降低成本
201第14章在线品牌管理的案例研究
205第15章移动应用推荐的案例研究
209第16章高科技产品制造商的案例研究
16．1处理缺失数据
16．2超越生产的应用
213第17章展望未来
17．1重复性研究
17．2隐私与公共数据集
17．3物联网
17．4未来的软件开发
17．5未来算法开发
17．6总结
221关于作者
223附录
225参考文献
231译者后记

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