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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From inside the book
Results 1-5 of 88
... Selection 75 2.5.3 Dimensionality Reduction 77 2.5.4 Numerosity Reduction 80 Data Discretization and Concept Hierarchy Generation 86 2.6.1 Discretization and Concept Hierarchy Generation for Numerical Data 88 2.6.2 Concept Hierarchy ...
... Selection Measures 296 6.3.3 Tree Pruning 304 6.3.4 Scalability and Decision Tree Induction 306 Bayesian Classification 310 6.4.1 Bayes' Theorem 310 6.4.2 Naïve Bayesian Classification 311 6.4.3 Bayesian Belief Networks 315 6.4.4 ...
... Selection 370 6.15.1 Estimating Confidence Intervals 370 6.15.2 ROC Curves 372 6.16 Summary 373 Exercises 375 Bibliographic Notes 378 Cluster Analysis 383 7.1 7.2 7.3 7.4 7.5 7.6 What Is Cluster Analysis? 383 Types of Data in Cluster ...
... selection(where data relevant to the analysis task are retrieved from the database) 4. Data transformation (where data are transformed or consolidated into forms appropriate for mining by performing summary or aggregation operations ...
... selection Database Data World Wide Other Info Warehouse Web Repositories Figure 1.5 Architecture of a typical data mining system. Knowledge base: This is the domain knowledge that is used to guide the search or evaluate 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 |