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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From inside the book
Results 1-5 of 88
... Transformation 67 2.4.1 Data Integration 67 2.4.2 Data Transformation 70 Data Reduction 72 2.5.1 Data Cube Aggregation 73 2.5.2 Attribute Subset Selection 75 2.5.3 Dimensionality Reduction 77 2.5.4 Numerosity Reduction 80 Data ...
... Transformation 427 7.8 Model-Based Clustering Methods 429 7.8.1 Expectation-Maximization 429 7.8.2 Conceptual Clustering 431 7.8.3 Neural Network Approach 433 7.9 Clustering High-Dimensional Data 434 7.9.1 CLIQUE: A Dimension-Growth ...
... transformation and data reduction are discussed, including the use of concept hierarchies for dynamic and static discretization. The automatic generation of concept hierarchies is also described. Chapters 3 and 4 provide a solid ...
... transformation (where data are transformed or consolidated into forms appropriate for mining by performing summary or aggregation operations, for instance)2 5. Data mining (an essential process where intelligent methods are applied in ...
... specify attributes to be included in the query, and the constraints on these attributes. A given query is transformed into a set of relational operations, such as join, selection, and projection, and is 10 Chapter 1 Introduction.
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 |