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
... Cuboid Downward 168 4.1.4 Star-cubing: Computing Iceberg Cubes Using a Dynamic Star-tree Structure 173 4.1.5 Precomputing Shell Fragments for Fast High-Dimensional OLAP 178 4.1.6 Computing Cubes with Complex Iceberg Conditions 187 ...
... cuboid. The base cuboid should correspond to an individual entity of interest, such as sales or customer. In other words, the lowest level should be usable, or useful for the analysis. A cube at the highest level of abstraction is the ...
... cuboids, so that a data cube may instead refer to a lattice of cuboids. Each higher level of abstraction further reduces the resulting data size. When replying to data mining requests, the smallest available cuboid relevant to the given ...
... cuboid Figure 3.3 Lattice of cuboids, making up a 4-D data cube for the dimensions time, item, location, and supplier. Each cuboid represents a different degree of summarization. as a cuboid. Given a set of dimensions, we can generate a ...
... cuboid in Figure 3.2 is the base cuboid for the given time,item,location, andsupplierdimensions. Figure 3.1 is a 3-D (nonbase) cuboid fortime,item, andlocation, summarized for all suppliers. The 0-D cuboid, which holds the highest level ...
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 |