数据挖掘教程

出版时间：2003-10  出版社：清华大学出版社  作者：邓纳姆  页数：315  字数：420000  
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内容概要

数据挖掘是近年来伴随着数据库系统的大量建立和万维网的广泛使用而发展起来的一门技术，它是数据库、机器学习与统计学这三个领域的交叉结合而形成的一门新兴技术。
    本书全面系统地介绍了各种数据挖掘的基本概念、方法和算法，是系统学习数据挖掘的一本好书。全书由四部分构成：第一部分是导论，全面介绍了数据挖掘的背景信息、相关概念以及数据挖掘所使用的主要技术；第二部分是数据挖掘的核心算法，系统深入地描述了用于分类、聚类和关联规则的常用算法；第三部分是数据挖掘的高级课题，主要叙述了Web挖掘、空间数据挖掘、时序数据和序列数据挖掘；第四部分是附录，介绍了目前市场上流行的一些数据挖掘工具产品，包括产品名称、产品功能、供应商、产吕所用技术、运行平台及产品状况。
    全书层分明、要念头清晰、表达准确、体系完整。书中对每种算法不仅进行了详尽的解释，还给出了算例及伪代码。每章后的练习和参考文献为读者提供了进一步思考相关问题的线索。
    本书适宜作为计算机专业研究生、高年级本科生教材，也可作为相关领域研究人员的参考书。

书籍目录

Part One Introduction 1 Introduction  1.1 Basic Data Mining Tasks  1.2 Data Mining Versus Knowledge Discovery in Databases  1.3 Data Mining Issues  1.4 Data Mining Metrics  1.5 Social Implications of Data Mining  1.6 Data Mining from a Database Perspective  1.7 The Future  1.8 Exercises  1.9 Bibliographic Notes 2 Related Concepts  2.1 Database/OLTP Systems  2.2 Fuzzy Sets and Fuzzy Logic  2.3 Information Retrieval  2.4 Decison Support Systems  2.5 Dimensional Modeling  2.6 Indexing  2.7 Data Warehousing  2.8 OLAP  2.9 Web Search Engines  2.10 Statistics  2.11 Machine Learning  2.12 Summary  2.13 Exercises  2.14 Bibliographic Notes 3 Data Mining Technques  3.1 Introduction  3.2 A Statistical Perspective on Data Mining  3.3 Similarity Measures  3.4 Decision Trees  3.5 Neural Networks  3.6 Genetic Algorithms  3.7 Exercises  3.8 Bibliographic NotesPart Two Core Topics  4 Classification  4.1 Introduction  4.2 Statistical-Based Algorthms  4.3 Dstance-Baesd Algorithms  4.4 Decision Tree-Based Algouithms  4.5 Neural Network-Based Algorithms  4.6 Rule-Based Algorithms  4.7 Combining Techniques  4.8 Summary  4.9 Exercises  4.10 Bibliographic Notes 5 Chustering  5.1 Introduction  5.2 Similarity and Distance Measures  5.3 Outliers  5.4 Hierarchcal Algorithms  5.5 Partitional Algorithms  5.6 Clustering Large Databases  5.7 Clustering with CATEGOUICAL attrebutes  5.8 Comparises  5.9 Exercise  5.10 Bibliographic Notes 6 Association Rules  6.1 Intrdrction  6.2 Large Itemsets  6.3 Basic Algorithms  6.4 Parallel and Distributed Algorithms  6.5 Comparing Apprlaches  6.6 Incremental Rules  6.7 Advanced Association Rule Techniques  6.8 Measuring the Quality of Rules  6.9 Exercises  6.10 Bibliographic NotesPart Three Advanced Topics  7 Web Mining  7.1 Introduction  7.2 Web Content Mining  7.3 Web Structure Mining  7.4 Web Usage Mining  7.5 Exercises  7.6 Bibliographic Notes 8 Spatial Mining  8.1 Introduction  8.2 Spatial Data Overview   8.3 Spatial Data Mining Primitives  8.4 Generalization and Specialization  8.5 Spatial Rules  8.6 Spatial Classification Algouithm  8.7 Spatial Clustering Algorithms   8.8 Exercises  8.9 Bibliographic Notes 9 Temporal Mining  9.1 Introduction  9.2 Modeling Temporal Events  9.3 Time Series  9.4 Pattern Detcdtion  9.5 Sequences  9.6 Temporal Association Rules  9.7 Exercises  9.8 Bibliographic NotesAPPENDICES A Data Mining Products A.1 Bibliographic NotesB Bibliography Index About the Author

用户评论 (总计2条)

•   在暑假的时候借来看过，感觉写的比较易懂，属于入门教程，基础概念清晰。虽然是英文版，但基本不影响阅读
•   居然不知道是英文版的。。。以后请写清楚哦。。。