Table of Contents
Abstract
Introduction
Technologies
Data Warehouse
Examples
Characteristics
Platforms
Summary
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Introduction
Competitiveness increasingly depends on the quality of decision making, so
it
is no wonder that companies often try to improve the quality of decisions
by
learning from past transactions and decisions. To support this process,
operational data generated by business transactions is consolidated in a
data
warehouse, which is often a relational database system (RDBMS). Users of
the
data warehouse should know what questions to ask in order to identify
patterns
and gather needed information. Data mining facilitates further exploration
by
discovering the implicit patterns in the data. Hence, data mining
significantly
augments the value of the data warehouse.
Data mining is relevant to many different types of businesses. For
example,
retail stores obtain profiles of customers and their buying patterns, and
supermarkets analyze their sales and the effect of advertising on sales.
Such
"target marketing" is becoming increasingly important. Data mining has
also
been applied in industries such as health care, insurance, and finance.
This paper describes data mining and some of the techniques used in data
mining, presents example applications, and mentions some of the
prerequisites
for employing data mining effectively in a business operation. This
discussion
shows that Silicon Graphics CHALLENGE servers are attractive data mining
platforms.
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