Data mining : concepts and techniques
著者
書誌事項
Data mining : concepts and techniques
（The Morgan Kaufmann series in data management systems）
Morgan Kaufmann Pub., an imprint of Elsevier, c2006
2nd ed
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注記
Includes bibliographical references (p. 703-743) and index
内容説明・目次
内容説明
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.
目次
1. Introduction
2. Data Preprocessing
3. Data Warehouse and OLAP Technology: An Overview
4. Data Cube Computation and Data Generalization
5. Mining Frequent Patterns, Associations, and Correlations
6. Classification and Prediction
7. Cluster Analysis
8. Mining Stream, Time-Series, and Sequence Data
9 Graph Mining, Social Network Analysis, and Multi-Relational Data Mining
10. Mining Object, Spatial, Multimedia, Text, and Web Data
11. Applications and Trends in Data Mining
Appendix A: An Introduction to Microsoft's OLE DB for Data Mining
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