Data Mining, Southeast Asia Edition
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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This book explores the concepts and techniques of data mining, a promising and
flourishing frontier in data and information systems and their applications. Data
mining, also popularly referred to as knowledge discovery from data (KDD), is the
The information and knowledge gained can be used for applications ranging
from market analysis, fraud detection, and customer retention, to production
control and science exploration. Data mining can be viewed as a result of the
The new database applications include handling spatial data (such as maps),
engineering design data (such as the design of buildings, system components, or
integrated circuits), hypertext and multimedia data (including text, image, video, ...
Database systems can be classified according to different criteria (such as data
models, or the types of data or applications involved), each of which may require
its own data mining technique. Data mining systems can therefore be classified ...
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8 Mining Stream TimeSeries and Sequence Data
9 Graph Mining Social Network Analysis and Multirelational Data Mining
10 Mining Object Spatial Multimedia Text and Web Data
11 Applications and Trends in Data Mining
An Introduction to Microsofts OLE DB for Data Mining