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 84
... Correlations 23 1.4.3 Classification and Prediction 24 1.4.4 Cluster Analysis 25 1.4.5 Outlier Analysis 26 1.4.6 Evolution Analysis 27 Are All of the Patterns Interesting? 27 Classification of Data Mining Systems 29 1.7 Data Mining Task ...
... Correlations 227 5.1 5.2 5.3 5.4 5.5 5.6 Basic Concepts and a Road Map 227 5.1.1 Market Basket Analysis: A Motivating ... Correlation Analysis 261 Constraint-Based Association Mining 265 5.5.1 Metarule-Guided Mining of Association Rules ...
... correlations in transactional and relational databases and data warehouses. In addition to introducing the basic ... correlation rules. Strategies for constraint-based mining and the use of interestingness measures to focus the rule ...
... correlation analysis, classification, prediction, cluster analysis, outlier analysis, and evolution analysis. Pattern evaluation module: This component typically employs interestingness measures (Section 1.5) and interacts with the data ...
... correlated or associated. Clusters and outliers can be identified by spatial cluster analysis. Moreover, spatial classification can be performed to construct models for prediction based on the relevant set of features of the spatial ...
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 |