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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Results 1-5 of 81
... Approach 351 6.10.3 Fuzzy Set Approaches 352 6.11 Prediction 354 6.11.1 Linear Regression 355 6.11.2 Nonlinear Regression 357 6.11.3 Other Regression-Based Methods 358 Chapter 7 6.12 6.13 6.14 6.15 Accuracy and Error Measures Contents xiii.
... Accuracy Measures 360 6.12.2 Predictor Error Measures 362 Evaluating the Accuracy of a Classifier or Predictor 363 6.13.1 Holdout Method and Random Subsampling 364 6.13.2 Cross-validation 364 6.13.3 Bootstrap 365 Ensemble Methods ...
... accuracy and how to choose the best classifier or predictor are discussed. In comparison with the corresponding chapter in the first edition, the sections on rule-based classification and support vector machines are new, and the ...
... accuracy of the discovered patterns can be poor. Data cleaning methods and data analysis methods that can handle noise are required, as well as outlier mining methods for the discovery and analysis of exceptional cases. Pattern ...
... accuracy and efficiency of mining algorithms involving distance measurements. Data reductioncan reduce the data size by aggregating, eliminating redundant features, or clustering, for instance. These techniques are not mutually ...
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 |