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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... Graph Mining, Social Network Analysis, and Multirelational Data Mining 535 9.1 9.2 9.3 9.4 Graph Mining 535 9.1.1 Methods for Mining Frequent Subgraphs 536 9.1.2 Mining Variant and Constrained Substructure Patterns 545 9.1.3 Applications: ...
... graph structured data, social network data, and multirelational data. The chapters are described briefly as follows ... graphs, social networks, multirelational data, spatiotemporal data, multimedia data, text data, and Web data. The ...
... Chapter 9 discusses methods for graph and structural pattern mining, social network analysis and multirelational data mining. Chapter 10 presents methods for mining object, spatial, multimedia, text, and Web data, which cover Preface xxiii.
... graph mining, social network analysis, and multirelational data mining. The chapters preceding the advanced topics are written to be as self-contained as possible, so they may be read in order of interest by the reader. All of the major ...
... graph mining section, and Xiaoxin Yin to the multirelational data mining section. Hong Cheng, Charios Ermopoulos, Hector Gonzalez, David J. Hill, Chulyun Kim, Sangkyum Kim, Chao Liu, Hongyan Liu, Kasif Manzoor, Tianyi Wu, Xifeng Yan ...
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 |