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 79
... Classification and Prediction Methods 290 Classification by Decision Tree Induction 291 6.3.1 Decision Tree Induction 292 6.3.2 Attribute Selection Measures 296 6.3.3 Tree Pruning 304 6.3.4 Scalability and Decision Tree Induction 306 ...
... classification and prediction, including decision tree induction, Bayesian classification, rule-based classification, the neural network technique of backpropagation, support vector machines, associative classification, k-nearest ...
... tree leaves represent classes or class distributions. Decision trees can easily be converted to classification rules. A neural network, when used for classification, is typically a collection of neuron-like processing units with ...
... classification is expressed in the form of a decision tree. The decision tree, for instance, may identify price as being the single factor that best distinguishes the three classes. The tree may reveal that, after price, other features ...
... classification, prediction, clustering, outlier analysis, or evolution analysis. The background knowledge to be used in ... decision trees, and cubes. A data mining query language can be designed to incorporate these primitives, allowing ...
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 |