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 94
... Subset Selection 75 2.5.3 Dimensionality Reduction 77 2.5.4 Numerosity Reduction 80 Data Discretization and Concept Hierarchy Generation 86 2.6.1 Discretization and Concept Hierarchy Generation for Numerical Data 88 2.6.2 Concept ...
... subsets of the data. Suppose that your job is to analyze the AllElectronics data. Through the use of relational queries, you can ask things like “Show me a list of all items that were sold in the last quarter.” Relational languages also ...
... subset of a data warehouse. It focuses on selected subjects, and thus its scope is department-wide. By providing multidimensional data views and the precomputation of summarized data, data warehouse systems are well suited for on-line ...
... subset selection(e.g., removing irrelevant attributes through correlation analysis), dimensionality reduction (e.g., using encoding schemes such as minimum length encoding or wavelets), and numerosity reduction (e.g., “replacing” the ...
... subsets, computing the measure for each subset, and then merging the results in order to arrive at the measure's value for the original (entire) data set. Both sum() and count() are distributive measures because they can be computed in ...
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 |