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 explosive growth in stored or transient data has generated an urgent need
for new techniques and automated tools that can intelligently assist us in
transforming the vast amounts of data into useful information and knowledge.
This book ...
Data transformations, such as normalization, may be applied. For example,
normalization may improve the accuracy and efficiency of mining algorithms
involving distance measurements. Data reductioncan reduce the data size by
should be transformed to blank). There are a number of different commercial
tools that can aid in the step of discrepancy detection.Data scrubbing tools use
simple domain knowledge (e.g., knowledge of postal addresses, and spell-
The tool performs discrepancy checking automatically in the background on the
latest transformed view of the data. Users can gradually develop and refine
transformations as discrepancies are found, leading to more effective and
In data transformation, the data are transformed or consolidated into forms
appropriate for mining. Data transformation can involve the following: Smoothing,
which works to remove noise from the data. Such techniques include binning, ...
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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