Data Mining, Southeast Asia EditionOur 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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... Neural Network 328 6.6.2 Defining a Network Topology 329 6.6.3 Backpropagation 329 6.6.4 Inside the Black Box: Backpropagation and Interpretability 334 Support Vector Machines 337 6.7.1 The Case When the Data Are Linearly Separable 337 ...
... neural network technique of backpropagation, support vector machines, associative classification, k-nearest neighbor classifiers, case-based reasoning, genetic algorithms, rough set theory, and fuzzy set approaches. Methods of ...
... neural networks (Figure 1.10). A decision treeis a flow-chart-like tree structure, where each node denotes a test on ... network, when used for classification, is typically a collection of neuron-like processing units with weighted ...
... neural network. sales campaign: good response, mild response, and no response. You would like to derive a model for each of these three classes based on the descriptive features of the items, such as price, brand, place made, type, and ...
... neural networks, nearest-neighbor classifiers, or clustering.1 Such methods provide better results if the data to be analyzed have been normalized, that is, scaled to a specific range such as [0.0, 1.0]. Your customer data, for example ...
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 |