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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... count, max (maximum), and min (minimum). These allow you to ask things like “Show me the total sales of the last month, grouped by branch,” or “How many sales transactions occurred in the month of December?” or “Which sales person had ...
... count or sales amount. The actual physical structure of a data warehouse may be a relational data store or a multidimensional data cube. A data cube provides a multidimensional view of data and allows the precomputation and fast ...
... count() are distributive measures because they can be computed in this manner. Other examples include max() and min ... count(). When computing (2.1) data cubes2, sum() and count() are typically saved in precomputation. 2.2 Descriptive ...
Jiawei Han, Jian Pei, Micheline Kamber. data cubes2, sum() and count() are typically saved in precomputation. Thus, the derivation of average for data cubes is straightforward. Sometimes, each value xi in a set may be associated with a ...
... count() in SQL), ∑x i (which is the sum() of xi), and then merged and to feed ∑x2i into (which the is the algebraic sum() of Equation x2i) (2.6). can be computed in any partition Thus the computation of the variance and standard ...
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 |