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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... Concept Hierarchy Generation 86 2.6.1 Discretization and Concept Hierarchy Generation for Numerical Data 88 2.6.2 Concept Hierarchy Generation for Categorical Data 94 Summary 97 Exercises 97 Bibliographic Notes 101 Data Warehouse and ...
... concept hierarchies for dynamic and static discretization. The automatic generation of concept hierarchies is also described. Chapters 3 and 4 provide a solid introduction to data warehouse, OLAP (On-Line Analytical Processing), and ...
... Concept hierarchies are a popular form of background knowledge, which allow data to be mined at multiple levels of abstraction. An example of a concept hierarchy for the attribute (or dimension) age is shown in Figure 1.14. User beliefs ...
... concept hierarchy for the attribute (or dimension) age. The root node represents the most Example 1.11 general abstraction level, denoted as all. There are several proposals on data mining languages and standards. In this book, we use a ...
... concept hierarchies locationhierarchy and age hierarchy be used as background knowledge to generalize branch locations and customer age values, respectively. Line 3 specifies that the kind of knowledge to be mined for this task is ...
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 |