Databases Reference
In-Depth Information
the major topics in a collection of documents and, for each document in the collection,
the major topics involved.
Increasingly large amounts of text and multimedia data have been accumulated and
made available online due to the fast growth of the Web and applications such as dig-
ital libraries, digital governments, and health care information systems. Their effective
search and analysis have raised many challenging issues in data mining. Therefore, text
mining and multimedia data mining, integrated with information retrieval methods,
have become increasingly important.
1.6
Which Kinds of Applications Are Targeted?
Where there are data, there are data mining applications
As a highly application-driven discipline, data mining has seen great successes in many
applications. It is impossible to enumerate all applications where data mining plays a
critical role. Presentations of data mining in knowledge-intensive application domains,
such as bioinformatics and software engineering, require more in-depth treatment and
are beyond the scope of this topic. To demonstrate the importance of applications as
a major dimension in data mining research and development, we briefly discuss two
highly successful and popular application examples of data mining:
business intelligence
and
search engines
.
1.6.1
Business Intelligence
It is critical for businesses to acquire a better understanding of the commercial context
of their organization, such as their customers, the market, supply and resources, and
competitors.
Business intelligence
(
BI
) technologies provide historical, current, and
predictive views of business operations. Examples include reporting, online analytical
processing, business performance management, competitive intelligence, benchmark-
ing, and predictive analytics.
“How important is business intelligence?”
Without data mining, many businesses may
not be able to perform effective market analysis, compare customer feedback on simi-
lar products, discover the strengths and weaknesses of their competitors, retain highly
valuable customers, and make smart business decisions.
Clearly, data mining is the core of business intelligence. Online analytical process-
ing tools in business intelligence rely on data warehousing and multidimensional data
mining. Classification and prediction techniques are the core of predictive analytics
in business intelligence, for which there are many applications in analyzing markets,
supplies, and sales. Moreover, clustering plays a central role in customer relationship
management, which groups customers based on their similarities. Using characteriza-
tion mining techniques, we can better understand features of each customer group and
develop customized customer reward programs.
