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.
|
From inside the book
Results 1-5 of 84
... (Figure 1.3). Thus, such a misnomer that carries both “data” and “mining” became a popular choice. Many other terms ... Figure 1.4 Data mining as a step in the process. Figure 1.3 Data mining—searching for knowledge (interesting patterns) ...
... Figure 1.4 and consists of an iterative sequence of the following steps: 1. Data cleaning (to remove noise and inconsistent data) 2. Data integration (where multiple data sources may be combined)1 3. Data selection(where data relevant ...
... Figure 1.9. From the relational database point of view, the sales table in Figure 1.9 is a nested relation because the attribute list of itemIDs contains a set of items. Because most relational database systems do not support nested ...
... Figure 1.11 shows a 2-D plot of customers with respect to customer locations in a city. Three clusters of data points are evident. Outlier. Analysis. A database may contain data objects that do not comply with the general behavior or ...
... Figure 2.2 Mean, median, and mode of symmetric versus positively and negatively skewed data. 2.2.2. where L1 is the ... Figure 2.2(a). However, data in most real applications are not symmetric. They may instead be either positively skewed ...
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 |
Other editions - View all
Common terms and phrases
Popular passages
References to this book
Geographic Data Mining and Knowledge Discovery Harvey J. Miller,Jiawei Han No preview available - 2003 |