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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Results 1-5 of 87
... 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.
... approach, frequent-pattern mining with vertical data format, and mining closed frequentitemsets. The chapter also ... approaches. Methods of regression are introduced. Issues regarding accuracy and how to choose the best classifier or ...
... approach you think is the most popular, and why. Describe three challenges to data mining regarding data mining methodology and user interaction issues. What are the major challenges of mining a huge amount of data (such as billions of ...
... Approach, Vols. 1–4, edited by Michalski et al. [MCM83, MCM86, KM90, MT94], C4.5: Programs for Machine Learning by Quinlan [Qui93], Elements of Machine Learning by Langley [Lan96], and Machine Learning by Mitchell [Mit97]. The book ...
... approach to heterogeneous database integration is to build wrappers and integrators (or mediators), on top of multiple, heterogeneous databases. When a query is posed to a client site, a metadata dictionary is used to translate the ...
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 |