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.
|
From inside the book
Results 1-5 of 84
... Mining Class Comparisons: Discriminating between Different Classes 210 4.3.5 Class Description: Presentation of Both ... Pattern Mining: A Road Map 232 Efficient and Scalable Frequent Itemset Mining Methods 234 5.2.1 The Apriori ...
Jiawei Han, Jian Pei, Micheline Kamber. Chapter 9 8.3 8.4 8.5 Mining Sequence Patterns in Transactional Databases 498 8.3.1 Sequential Pattern Mining: Concepts and Primitives 498 8.3.2 Scalable Methods for Mining Sequential Patterns 500 ...
... data warehouses, the Web, other massive information repositories, or data streams. Data mining is a multidisciplinary field, drawing work from areas including database technology, machine learning, statistics, pattern recognition ...
... data warehouse and on-line analytical processing, as well as the relationship between data warehousing and data mining. Chapter 4 takes a more in-depth look at data warehouse and OLAP technology, presenting a detailed study of methods of ...
... data, which cover a great deal of new progress in these areas. Finally, in Chapter 11, we summarize the concepts presented in this book and discuss applications and trends in data mining. New material has been added on data mining for ...
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 |
Other editions - View all
Common terms and phrases
Popular passages
References to this book
Geographic Data Mining and Knowledge Discovery Harvey J. Miller,Jiawei Han No preview available - 2003 |