Data Mining
Tags
Summary
Editor's review
From a professional perspective, the book's strength lies in its clear logical framework and moderate theoretical depth. It does not get bogged down in cumbersome mathematical derivations but focuses on explaining the principles and practical significance of methods, enabling readers to quickly build an overall understanding of the field of data mining. The cases selected in the book are close to reality and help readers understand how technology solves business and scientific problems.
However, the book's limitation is that it covers relatively little frontier progress (such as the application of deep learning in data mining), making it more suitable as a systematic reader for the introductory stage. Its value lies in laying a solid foundation for readers and cultivating data thinking rather than serving as a manual of cutting-edge technology. Overall, this is a reliable teaching reference book, especially suitable for readers who need a systematic introduction or who teach related courses.
Who it's for
A good fit for
- Lower-division undergraduate students in computer science or data science-related majors
- Technology enthusiasts who want a systematic understanding of the basic concepts and methods of data mining
- Business analysts with non-technical backgrounds who need to understand the logic of data mining
- University teachers who teach related courses and need a clearly structured textbook
Not a good fit for
- Senior researchers seeking implementation details of advanced algorithms or the latest research developments
- Purely hands-on developers who want to quickly master practical programming skills and urgently need code examples
Reading tips
- Read in chapter order, focusing on understanding the core ideas and applicable scenarios of each method
- Combine reading with preliminary practice using the simple datasets provided in the book or public standard datasets
- When encountering algorithm descriptions, try reinterpreting their application value using business scenarios you are familiar with
About the publisher
The Publishing House of Electronics Industry is a comprehensive publisher distinguished by its focus on electronic information and related fields. Judging from the more than 3,000 titles in its catalogue, its publishing covers programming and design, artificial intelligence, computer science, finance and economics, photography, and social sciences, attending both to cutting-edge technology and to general readers. Representative titles include Factor Investing: Methods and Practice, x86 Assembly Language: From Real Mode to Protected Mode, The RISC-V Reader, Artificial Intelligence: Speech Recognition, Understanding and Practice, Phased Array Radar Technology, Radar Target Characteristics, and 100 Go Mistakes and How to Avoid Them, most of which centre on technical methods and engineering practice. Its authors include Shi Chuan, Li Zhong, Wang Xiaobo, David Patterson, Andrew Waterman, and Yu Zihao, among others from China and abroad. Owing to limited public information, no further publishing features are elaborated here.
Contents
- 版权信息
- 内容简介
- 编写组
- 总序
- 前言
- 第1章 绪论
- 1.1 数据挖掘概述
- 1.2 数据挖掘起源及发展历史
- 1.3 数据挖掘常用工具
- 1.4 数据挖掘应用场景
- 习题
- 参考文献
- 第2章 数据预处理与相似性
- 2.1 数据类型
- 2.2 数据预处理
- 2.3 数据的相似性
- 习题
- 参考文献
- 第3章 分类
- 3.1 分类的基本概念、分类过程及分类器性能的评估
- 3.2 决策树
- 3.3 贝叶斯分类
- 3.4 支持向量机
- 3.5 实战：决策树算法在Weka中的实现
- 习题
- 参考文献
- 第4章 回归
- 4.1 回归概述
- 4.2 一元回归分析
- 4.3 多元线性回归分析
- 4.4 逻辑回归分析
- 4.5 其他回归分析
- 4.6 实战：用回归分析方法给自己的房子定价
- 习题
- 参考文献
- 第5章 聚类
- 5.1 聚类概述
- 5.2 划分方法
- 5.3 层次方法
- 5.4 基于密度的方法
- 5.5 实战：聚类分析
- 习题
- 参考文献
- 第6章 关联规则
- 6.1 概述
- 6.2 Apriori算法：通过限制候选项集产生发现频繁项集
- 6.3 FP-growth算法
- 6.4 其他关联规则算法
- 6.5 实战：个人信用关联规则挖掘
- 习题
- 参考文献
- 第7章 常用大数据挖掘算法优化改进
- 7.1 分类算法
- 7.2 聚类算法
- 7.3 关联规则
- 习题
- 参考文献
- 第8章 推荐系统
- 8.1 推荐系统概述
- 8.2 基于内容的推荐
- 8.3 协同过滤
- 8.4 其他推荐技术
- 8.5 实战：基于协同过滤算法推荐电影
- 习题
- 参考文献
- 第9章 互联网数据挖掘
- 9.1 链接分析与网页排序
- 9.2 互联网信息抽取
- 9.3 日志挖掘与查询分析
- 习题
- 参考文献
- 附录A 数据挖掘工具Weka
- A.1 Weka简介
- A.2 Explorer界面
- A.3 Knowledge Flow界面
- A.4 Experimenter界面
- 习题
- 参考文献
- 附录B Spark 机器学习库MLlib
- B.1 Spark简介
- B.2 Spark RDD
- B.3 Spark MLlib简介
- B.4 Spark MLlib数据类型
- B.5 Spark MLlib算法库
- 习题
- 参考文献
- 附录C 大数据和人工智能实验环境
FAQ
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About the author
Public information about Wang Zhaoxia is limited, and what is available comes mainly from the six works published under her name. These works point in two directions. One is computing and data, including Data Mining and its second edition, which focus on methods and techniques and serve related courses and examinations. The other is music education and aesthetic education, covering an introduction to school music education and its teaching materials and methods, the alignment between college music teacher education and basic music education, and the professional quality of music teachers. Among these, the themes of rural music volunteer teaching and aesthetic education immersion reflect her sustained attention to combining college-level music teacher education with rural practice. Overall, her works combine the rigor of textbooks with a practical orientation and are suitable for teaching and reference reading.
