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
... Categorical Data 94 Summary 97 Exercises 97 Bibliographic Notes 101 Data Warehouse and OLAP Technology: An Overview 105 3.1 3.2 What Is a Data Warehouse? 105 3.1.1 Differences between Operational Database Systems and Data Warehouses 108 ...
... Categorical, Ordinal, and Ratio-Scaled Variables 392 7.2.4 Variables of Mixed Types 395 7.2.5 Vector Objects 397 A Categorization of Major Clustering Methods 398 Partitioning Methods 401 7.4.1 Classical Partitioning Methods: k-Means and ...
... categorical (discrete, unordered) labels, prediction models continuous-valued functions. That is, it is used to predict missing or unavailable numerical data values rather than class labels. Although the term prediction may refer to ...
... categorical response labels for each store item, you would like to predict the amount of revenue that each item will generate during an upcoming sale at AllElectronics, based on previous sales data. This is an example of (numeric) ...
... from numerical data using data discretization techniques. The automatic generation of concept hierarchies from categorical data is also described. 2.1 Why Preprocess the Data? Imagine that you are a. 47 2 Data Preprocessing.
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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References to this book
Geographic Data Mining and Knowledge Discovery Harvey J. Miller,Jiawei Han No preview available - 2003 |