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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Moreover, data warehouses provide on-line analytical processing (OLAP) tools
for the interactive analysis of multidimensional data of varied granularities, which
facilitates effective data generalization and data mining. Many other data mining
...
These systems are known as on-line analytical processing (OLAP) systems. The
major distinguishing features between OLTP and OLAP are summarized as
follows: Users and system orientation: An OLTP system is customer-oriented ...
Other OLAP operations: Some OLAP systems offer additional drilling operations.
For example,drill-acrossexecutes queries involving (i.e., across) more than one
fact table. The drill-through operation uses relational SQL facilities to drill through
...
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This is a good book. Lot of thinking work is needed to read such books. As they say....its all in your head.
data ming
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 |