书籍详情
数据挖掘教程
作者：（美）Margaret H.Dunham著
出版社：清华大学出版社
出版时间：2003-01-01
ISBN：9787302073833
定价：¥34.80
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内容简介
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？鞘萃诰虻暮诵乃惴ǎ低成钊氲孛枋隽擞糜诜掷唷⒕劾嗪凸亓嬖虻某Ｓ盟惴ǎ坏谌糠质鞘萃诰虻母呒犊翁猓饕鹗隽薟eb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。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。
作者简介
暂缺《数据挖掘教程》作者简介
目录
Part One Introduction
Introduction
1.1 Basic Dam Mining Tasks
1.1.1 C1aSSi6cation
1.1.2 Regression
1.1.3 Time Series Analysis
1.1.4 Prediction
1.1.5 Clustering
1.1.6 Summarization
1.1.7 Association Rules
1.1.8 Sequence Discovery
1.2 Data Mining Versus Knowledge Discovery in Databases
1.2.1 The Development Of Data Mining
1.3 Dam Mining Issues
1.4 Data Mining Metrics
1.5 Social Implications Of Data Mining
1.6 Dam Mining from a Database Perspec6ve
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 1nformiton Retrieval
2.4 Decision Support Systems
2.5 Dimensional MOdenn9
2.5.1 Multidimensional Schemas
2.5.2 1ndexing
2.6 Data Warehousing
2.7 OLAP.
2.8 Web Search Engines
2.9 Statistics
2.10 Machine Leaning
2.11 Pattern Matching
2.12 Summary
2.13 Exercises
2.14 Bibliographic Notes
3 Data Mining Techniques
3.1 Introduction
3.2 A Statistical Perspective on Data Mining
3.2.1 Point Estimation
3.2.2 Models Based on Summarization
3.2.3 Bayes Theorem
3.2.4 Hypothesis Testing
3.2.5 Regression and Correlation
3.3 Similarity Measures
3.4 Decision Trees
3.5 Neural Networks
3.5.1 Activation Functions
3.6 Genetic Algorithms
3.7 Exercises
3.8 Bibliographic Notes
Part Two Core Topics
4 Classification
4.1 Introduction
4.1.1 Issues in Classification
4.2 Statistical-Based Algorithms
4.2.1 Regression
4.2.2 Bayesian Classification
4.3 Distance-Based Algorithms
4.3.1 Simple Approach
4.3.2 K Nearest Neighbors
4.4 Decision Tree-Based Algorithms
4.4.1 ID3
4.4.2 C4.5 and C5.0
4.4.3 CART
4.4.4 Scalable DT Techniques
4.5 Neural Network—Based Algorithms
4.5.1 Propagation
4.5.2 NN Supervised Learning
4.5.3 Radial Basis Function Networks
4.5.4 Perceptrons
4.6 Rule-Based Algorithms
4.6.1 Generating Rules from a DT
4.6.2 Generating Rules from a Neural Net
4.6.3 Generating Rules Without a DT or NN
4.7 Combining Techniques
4.8 Summary
4.9 Exercises
4.10 Bibliographic Notes
5 Clustering
5.1 Introduction
5.2 Similarity and Distance Measures
5.3 Outliers
5.4 Hierarchical Algorithms
5.4.1 Agglomerative Algorithms
5.4.2 Divisive Clustering
5.5 Partitional Algorithms
5.5.1 Minimum Spanning Tree
5.5.2 Squared Error Clustering Algorithm
5.5.3 K-Means Clustering
5.5.4 Nearest Neighbor Algorithm
5.5.5 PAM Algorithm
5.5.6 Bond Energy Algorithm
5.5.7 Clustering with Genetic Algorithms
5.5.8 Clustering with Neural Networks
5.6 Clustering Large Databases
5.6.1 BIRCH
5.6.2 DBSCAN
5.6.3 CURE Algorithm
5.7 Clustering with Categorical Attributes
5.8 Comparison
5.9 Exercises
5.10 Bibliographic Notes
6 Association Rules
6.1 Introduction
6.2 Large Itemsets
6.3 Basic Algorithms
6.3.1 Apriori Algorithm
6.3.2 Sampling Algorithm
6.3.3 Partitioning
6.4 Parallel and Distributed Algorithms
6.4.1 Data Parallelism
6.4.2 Task Parallelism
6.5 Comparing Approaches
6.6 Incremental Rules
6.7 Advanced Association Rule Techniques
6.7.1 Generalized Association Rules
6.7.2 Multiple-Level Association Rules
6.7.3 Quantitative Association Rules
6.7.4 Using Multiple Minimum Supports
6.7.5 Correlation Rules
6.8 Measuring the Quality of Rules
6.9 Exercises
6.10 Bibliographic Notes
Part Three Advanced Topics
7 Web Mining
7.1 Introduction
7.2 Web Content Mining
7.2.1 Crawlers
7.2.2 Harvest System
7.2.3 Virtual Web View
7.2.4 Personalization
7.3 Web Structure Mining
7.3.1 Page Rank
7.3,2 Clever
7.4 Web Usage Mining
7.4,1 Preprocessing
7.4.2 Data Structures
7,4.3 Pattern Discovery
7.4.4 Pattern Analysis
7.5 Exercises
7.6 Bibliographic Notes
8 Spatial Mining
8.1 Introduction
8.2 Spatial Data Overview
8.2.1 Spatial Queries
8.2.2 Spatial Data Structures
8.2.3 Thematic Maps
8.2.4 Image Databases
8.3 Spatial Data Mining Primitives
8.4 Generalization and Specialization
8.4.1 Progressive Refinement
8.4.2 Generalization
8.4.3 Nearest Neighbor
8.4.4 STING
8.5 Spatial Rules
8.5.1 Spatial Association Rules
8.6 Spatial Classification Algorithm
8.6.1 ID3 Extension
8,6.2 Spatial Decision Tree
8.7 Spatial Clustering Algorithms
8.7,1 CLARANS Extensions
8.7.2 SD(CLARANS)
8.7.3 DBCLASD
8.7.4 BANG
8.7.5 Wave Cluster
8.7.6 Approximation
8.8 Exercises
8.9 Bibliographic Notes
9 Temporal Mining
9.1 Introduction
9.2 Modeling Temporal Events
9.3 Time Series
9.3.1 Time Series Analysis
9.3.2 Trend Analysis
9.3.3 Transformation
9.3.4 Similarity
9.3.5 Prediction
9.4 Pattern Detection
9.4.1 String Matching
9.5 Sequences
9.5.1 AprioriAll
9.5.2 SPADE
9.5.3 Generalization
9.5.4 Feature Extraction
9.6 Temporal Association Rules
9.6.1 Intertransaction Rules
9.6.2 Episode Rules
9.6.3 Trend Dependencies
9.6.4 Sequence Association Rules
9.6.5 Calendric Association Rules
9.7 Exercises
9.8 Bibliographic Notes
APPENDICES
A Data Mining Products
A.1 Bibliographic Notes
B Bibliography
Index
About the Author
Introduction
1.1 Basic Dam Mining Tasks
1.1.1 C1aSSi6cation
1.1.2 Regression
1.1.3 Time Series Analysis
1.1.4 Prediction
1.1.5 Clustering
1.1.6 Summarization
1.1.7 Association Rules
1.1.8 Sequence Discovery
1.2 Data Mining Versus Knowledge Discovery in Databases
1.2.1 The Development Of Data Mining
1.3 Dam Mining Issues
1.4 Data Mining Metrics
1.5 Social Implications Of Data Mining
1.6 Dam Mining from a Database Perspec6ve
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 1nformiton Retrieval
2.4 Decision Support Systems
2.5 Dimensional MOdenn9
2.5.1 Multidimensional Schemas
2.5.2 1ndexing
2.6 Data Warehousing
2.7 OLAP.
2.8 Web Search Engines
2.9 Statistics
2.10 Machine Leaning
2.11 Pattern Matching
2.12 Summary
2.13 Exercises
2.14 Bibliographic Notes
3 Data Mining Techniques
3.1 Introduction
3.2 A Statistical Perspective on Data Mining
3.2.1 Point Estimation
3.2.2 Models Based on Summarization
3.2.3 Bayes Theorem
3.2.4 Hypothesis Testing
3.2.5 Regression and Correlation
3.3 Similarity Measures
3.4 Decision Trees
3.5 Neural Networks
3.5.1 Activation Functions
3.6 Genetic Algorithms
3.7 Exercises
3.8 Bibliographic Notes
Part Two Core Topics
4 Classification
4.1 Introduction
4.1.1 Issues in Classification
4.2 Statistical-Based Algorithms
4.2.1 Regression
4.2.2 Bayesian Classification
4.3 Distance-Based Algorithms
4.3.1 Simple Approach
4.3.2 K Nearest Neighbors
4.4 Decision Tree-Based Algorithms
4.4.1 ID3
4.4.2 C4.5 and C5.0
4.4.3 CART
4.4.4 Scalable DT Techniques
4.5 Neural Network—Based Algorithms
4.5.1 Propagation
4.5.2 NN Supervised Learning
4.5.3 Radial Basis Function Networks
4.5.4 Perceptrons
4.6 Rule-Based Algorithms
4.6.1 Generating Rules from a DT
4.6.2 Generating Rules from a Neural Net
4.6.3 Generating Rules Without a DT or NN
4.7 Combining Techniques
4.8 Summary
4.9 Exercises
4.10 Bibliographic Notes
5 Clustering
5.1 Introduction
5.2 Similarity and Distance Measures
5.3 Outliers
5.4 Hierarchical Algorithms
5.4.1 Agglomerative Algorithms
5.4.2 Divisive Clustering
5.5 Partitional Algorithms
5.5.1 Minimum Spanning Tree
5.5.2 Squared Error Clustering Algorithm
5.5.3 K-Means Clustering
5.5.4 Nearest Neighbor Algorithm
5.5.5 PAM Algorithm
5.5.6 Bond Energy Algorithm
5.5.7 Clustering with Genetic Algorithms
5.5.8 Clustering with Neural Networks
5.6 Clustering Large Databases
5.6.1 BIRCH
5.6.2 DBSCAN
5.6.3 CURE Algorithm
5.7 Clustering with Categorical Attributes
5.8 Comparison
5.9 Exercises
5.10 Bibliographic Notes
6 Association Rules
6.1 Introduction
6.2 Large Itemsets
6.3 Basic Algorithms
6.3.1 Apriori Algorithm
6.3.2 Sampling Algorithm
6.3.3 Partitioning
6.4 Parallel and Distributed Algorithms
6.4.1 Data Parallelism
6.4.2 Task Parallelism
6.5 Comparing Approaches
6.6 Incremental Rules
6.7 Advanced Association Rule Techniques
6.7.1 Generalized Association Rules
6.7.2 Multiple-Level Association Rules
6.7.3 Quantitative Association Rules
6.7.4 Using Multiple Minimum Supports
6.7.5 Correlation Rules
6.8 Measuring the Quality of Rules
6.9 Exercises
6.10 Bibliographic Notes
Part Three Advanced Topics
7 Web Mining
7.1 Introduction
7.2 Web Content Mining
7.2.1 Crawlers
7.2.2 Harvest System
7.2.3 Virtual Web View
7.2.4 Personalization
7.3 Web Structure Mining
7.3.1 Page Rank
7.3,2 Clever
7.4 Web Usage Mining
7.4,1 Preprocessing
7.4.2 Data Structures
7,4.3 Pattern Discovery
7.4.4 Pattern Analysis
7.5 Exercises
7.6 Bibliographic Notes
8 Spatial Mining
8.1 Introduction
8.2 Spatial Data Overview
8.2.1 Spatial Queries
8.2.2 Spatial Data Structures
8.2.3 Thematic Maps
8.2.4 Image Databases
8.3 Spatial Data Mining Primitives
8.4 Generalization and Specialization
8.4.1 Progressive Refinement
8.4.2 Generalization
8.4.3 Nearest Neighbor
8.4.4 STING
8.5 Spatial Rules
8.5.1 Spatial Association Rules
8.6 Spatial Classification Algorithm
8.6.1 ID3 Extension
8,6.2 Spatial Decision Tree
8.7 Spatial Clustering Algorithms
8.7,1 CLARANS Extensions
8.7.2 SD(CLARANS)
8.7.3 DBCLASD
8.7.4 BANG
8.7.5 Wave Cluster
8.7.6 Approximation
8.8 Exercises
8.9 Bibliographic Notes
9 Temporal Mining
9.1 Introduction
9.2 Modeling Temporal Events
9.3 Time Series
9.3.1 Time Series Analysis
9.3.2 Trend Analysis
9.3.3 Transformation
9.3.4 Similarity
9.3.5 Prediction
9.4 Pattern Detection
9.4.1 String Matching
9.5 Sequences
9.5.1 AprioriAll
9.5.2 SPADE
9.5.3 Generalization
9.5.4 Feature Extraction
9.6 Temporal Association Rules
9.6.1 Intertransaction Rules
9.6.2 Episode Rules
9.6.3 Trend Dependencies
9.6.4 Sequence Association Rules
9.6.5 Calendric Association Rules
9.7 Exercises
9.8 Bibliographic Notes
APPENDICES
A Data Mining Products
A.1 Bibliographic Notes
B Bibliography
Index
About the Author
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