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 evaluation of pattern interestingness as deep as possible into the mining
process so as to confine the search to only the interesting patterns. User interface
:This module communicates between users and the data mining system, allowing
A data mining query is defined in terms of data mining task primitives. These
primitives allow the user to interactively communicate with the data mining
system during discovery in order to direct the mining process, or examine the
findings from ...
Domain knowledge related to databases, such as integrity constraints and
deduction rules, can help focus and speed up a data mining process, or judge
the interestingness of discovered patterns. Data mining query languages and ad
“How can the data be preprocessed in order to help improve the quality of the
data and, consequently, of the mining results? How can the data be
preprocessed so as to improve the efficiency and ease of the mining process?”
There are a ...
Such an overview is essential for understanding the overall data mining and
knowledge discovery process. In this chapter, we study a well-accepted definition
of the data warehouse and see why more and more organizations are building
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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