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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... cell stores the value of some aggregate measure, such as count or sales amount. The actual physical structure of a ... cell of the cube issales amount(in thousands). For example, the total salesforthefirstquarter,Q1 ...
... cell values are shown. 1.3.3 Transactional Databases In general, atransactional databaseconsists of a file where each record represents a transaction. A transaction typically includes a unique transaction identity number (transID) and a ...
... cell (or slot) in the table. The χ2 value statistic) is computed as: i=1 χ2 = ∑c∑r. )2 j=1 (oij−eijeij, (2.9) where oij ... cells that contribute the most to theχ2 value are those whose actual count is very different from that expected ...
... cell. For example, the expected frequency for the cell (male, fiction) is e 11 = count(male)×count(fiction) N = 300×450 1500 = 90, and so on. Notice that in any row, the sum of the expected frequencies must equal the total observed ...
... cell holds an aggregate data value, corresponding to the data point in multidimensional space. (For readability, only some cell values are shown.) Concept Year 2004 Quarter Sales YearQ1 2003 Q2 Sales Q1 Q2 2.5 Data Reduction 73.
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 |