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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... example. A relational database for AllElectronics. The AllElectronics company is described by the following relation tables: customer, item, employee, and branch. Fragments of the tables described here are shown in Figure 1.6. The ...
... example, rather than storing the details of each sales transaction, the data warehouse may store a summary of the transactions per item type for each store or, summarized to a higher level, for each sales region. A data warehouse is ...
... example, time may be decomposed according to fiscal years, academic years, or calendar years. Years may be further decomposed into quarters or months. Spatial. Databases. and. Spatiotemporal. Databases. Spatial databases contain spatial ...
... example, we may like to detect intrusions of a computer network based on the anomaly of message flow, which may be discovered by clustering data streams, dynamic construction of stream models, or comparing the current frequent patterns ...
... example, in the AllElectronics store, classes of items for sale includecomputersandprinters, and concepts of ... Example 1.4 Example 1.5 the target class) in general terms, 1.4 Data Mining Functionalities—What Kinds of Patterns Can Be ...
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 |