Data Mining, Southeast Asia EditionElsevier, 2006 M04 6 - 800 pages Our ability to generate and collect data has been increasing rapidly. Not only are all of our business, scientific, and government transactions now computerized, but the widespread use of digital cameras, publication tools, and bar codes also generate data. On the collection side, scanned text and image platforms, satellite remote sensing systems, and the World Wide Web have flooded us with a tremendous amount of data. This explosive growth has generated an even more urgent need for new techniques and automated tools that can help us transform this data into useful information and knowledge. Like the first edition, voted the most popular data mining book by KD Nuggets readers, this book explores concepts and techniques for the discovery of patterns hidden in large data sets, focusing on issues relating to their feasibility, usefulness, effectiveness, and scalability. However, since the publication of the first edition, great progress has been made in the development of new data mining methods, systems, and applications. This new edition substantially enhances the first edition, and new chapters have been added to address recent developments on mining complex types of data— including stream data, sequence data, graph structured data, social network data, and multi-relational data.
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Jiawei Han, Jian Pei, Micheline Kamber. Chapter 1. Contents. Foreword xix Preface xxi Introduction 1 1.1 1.2 1.3 1.5 1.6 1.8 What Motivated Data Mining? Why ... Chapter 2 Chapter 3 1.10 Summary 39 Exercises 40 Bibliographic ix Table of ...
Jiawei Han, Jian Pei, Micheline Kamber. Chapter 2 Chapter 3 1.10 Summary 39 Exercises 40 Bibliographic Notes 42 Data Preprocessing 47 2.1 2.2 2.3 2.4 2.5 2.6 2.7 Why Preprocess the Data? 48 Descriptive Data Summarization 51 2.2.1 ...
... Chapter 4 Data Cube Computation and Data Generalization 157 4.1 4.2 Efficient Methods for Data Cube Computation 157 4.1.1 A Road Map for the Materialization of Different Kinds of Cubes 158 4.1.2 Multiway Array Aggregation for Full Cube ...
Jiawei Han, Jian Pei, Micheline Kamber. Chapter 5 4.3 4.4 Attribute-Oriented Induction—An Alternative Method for Data Generalization and Concept Description 198 4.3 ... Chapter 6 Classification and Prediction 285 6.1 6.2 6.3 6.4 xii Contents.
Jiawei Han, Jian Pei, Micheline Kamber. Chapter 6 Classification and Prediction 285 6.1 6.2 6.3 6.4 6.5 6.6 6.7 6.8 6.9 6.10 What Is Classification? What Is ... Chapter 7 6.12 6.13 6.14 6.15 Accuracy and Error Measures Contents xiii.
Contents
1 | |
47 | |
105 | |
4 Data Cube Computation and Data Generalization | 157 |
5 Mining Frequent Patterns Associations and Correlations | 227 |
6 Classification and Prediction | 285 |
7 Cluster Analysis | 383 |
8 Mining Stream TimeSeries and Sequence Data | 467 |
9 Graph Mining Social Network Analysis and Multirelational Data Mining | 535 |
10 Mining Object Spatial Multimedia Text and Web Data | 591 |
11 Applications and Trends in Data Mining | 649 |
An Introduction to Microsofts OLE DB for Data Mining | 691 |
Bibliography | 703 |
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Geographic Data Mining and Knowledge Discovery Harvey J. Miller,Jiawei Han No preview available - 2003 |