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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The implementation methods discussed are particularly oriented toward the
development of scalable and efficient data mining tools. In this chapter, you will
learn how data mining is part of the natural evolution of database technology,
Performance issues: These include efficiency, scalability, and parallelization of
data mining algorithms. ... To effectively extract information from a huge amount
of data in databases, data mining algorithms must be efficient and scalable.
How can the data be preprocessed so as to improve the efficiency and ease of
the mining process?” There are a number of data preprocessing techniques.
Data cleaning can be applied to remove noise and correct inconsistencies in the
An overview of data warehouse implementation examines general strategies for
efficient data cube computation, OLAP data indexing, and OLAP query
processing. Finally, we look at on-line-analytical mining, a powerful paradigm
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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