– Part III
19
Chapter 34 Data Mining
Review Questions
34.1 Discuss what data mining represents.
Data mining is concerned with the analysis of data and the use of software techniques for
See Section 34.1.
34.2 Provide examples of data mining applications.
Table 34.2 Examples of data mining applications.
Retail/Marketing
Identifying buying patterns of customers
Banking
Detecting patterns of fraudulent credit card use
– Part III
20
See Section 34.2.
34.3 Describe how the following data mining operations are applied and provide typical examples
for each.
34.4 Describe the main aims and phases of the CRISP-DM model.
The major aims of CRISP-DM are to make large data mining projects run more efficiently, be
cheaper, more reliable, and more manageable.
The CRISP-DM methodology is a hierarchical process model. At the top level, the process is
Table 34.3 Phases of the CRISP-DM Model.
Phase
– Part III
21
Business understanding
Data understanding
34.5 Provide examples of important features of data mining tools.
There are a growing number of commercial data mining tools on the marketplace. The
34.6 Discuss the relationship between data warehousing and data mining.
One of the major challenges for organizations seeking to exploit data mining is identifying
suitable data to mine. Data mining requires a single, separate, clean, integrated, and self–
consistent source of data. A data warehouse is well equipped for providing data for mining for
the following reasons:
Data quality and consistency is a prerequisite for mining to ensure the accuracy of the
predictive models. Data warehouses are populated with clean, consistent data.
– Part III
22
34.7 Discuss how Oracle supports data mining.
Exercises
34.8 Consider how a company such as DreamHome could benefit from data mining. Discuss, using
examples, the data mining operations which could be most usefully applied within DreamHome.
34.9 Investigate whether your organization (such as your University/College or workplace) has invested
in data mining technologies and if yes whether the data mining tool(s) forms part of a larger
investment in business intelligence technologies. If possible, establish the reasons for the interest in
data mining, how the tools are being applied, and the whether the promise of data mining has been
realized.