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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Although this book assumes that readers have basic knowledge of information
systems, we provide a brief introduction to each of the major data repository
systems listed above. In this section, we also introduce the fictitious AllElectronics
Suppose that AllElectronics is a successful international company, with branches
around the world. Each branch has its own set of databases. The president of
AllElectronics has asked you to provide an analysis of the company's sales per ...
A transactional database for AllElectronics. Transactions can be stored in a table,
with one record per transaction. A fragment of a transactional database for
AllElectronics is shown in Figure 1.9. From the relational database point of view,
An example of such a rule, mined from the AllElectronics transactional database,
is buys(X,“computer”) ⇒ buys(X,“software”) [support = 1%,confidence = 50%]
where X is a variable representing a customer. A confidence, or certainty, of 50%
Table 3.2 A 2-D view of sales data for AllElectronics according to the dimensions
time and item, where the sales are from branches located in the city of Vancouver
. The measure displayed is dollars sold (in thousands). location = “Vancouver” ...
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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