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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In this chapter, you will learn how data mining is part of the natural evolution of
database technology, why data mining is important, and how it is defined. You
will learn about the general architecture of data mining systems, as well as gain ...
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
Examples for Defining Star, Snowflake, and Fact Constellation Schemas “How
can I define a multidimensional schema for my data?” Just as relational query
languages like SQL can be used to specify relational queries, a data mining
A define dimension statement is used to define each of the dimensions.
Snowflake schema definition. The snowflake schema of Example 3.2 and Figure
3.5 is defined in DMQL as follows: define cube sales snowflake [time, item,
define cube shipping [time, item, shipper, from location, to location]: dollarscost =
sum(cost in dollars), units shipped = count(*) define dimension time as time in
cube sales define dimension item as item in cube sales define dimension shipper
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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