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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From inside the book
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The schema graph resembles a starburst, with the dimension tables displayed in
a radial pattern around the central fact table. Star schema.A starschema
forAllElectronicssales is shown in Figure 3.4. Sales are
consideredalongfourdimensions ...
Figure 3.4 Example 3.2 time dimension table sales fact table item dimension
table time_ key time_key item_key day item_key item_name day_of_the_week
branch_key brand month location_key type quarter dollars_sold supplier_type
year ...
Figure 3.5 Example 3.3 time dimension table time_key day day_of_week month
quarter year supplier dimension table supplier_key supplier_type sales fact table
time_key item_key branch_key location_key dollars_sold units_sold item ...
Figure 3.6 3.2.3 Example 3.4 time sales item shipping shipper dimension table
time_key fact table time_key dimension table item_key fact table item_key
dimension table shipper_key day item_key item_name time_key shipper_name ...
Example 3.5 Example 3.6 define dimension time as (time key, day, day of week,
month, quarter, year) define dimension item as (item key, ... Definingsupplierin
this way implicitly creates asupplier keyin the item dimension table definition.
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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 |