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Unit 9: Data Mining

2. ........................ is arguably a form of data mining, which automatically brings relevant Notes
messages to the surface from a chaotic sea of phishing attempts and Viagra pitches.
3. ........................ is a phase in which noise data and irrelevant data are removed from the
collection.
4. ........................ is a phase in which the selected data is transformed into forms appropriate
for the mining procedure.
5. ........................ is the final phase in which the discovered knowledge is visually represented
to the user.

6. ........................ are actually the most common data source for data mining algorithms,
especially at the research level.
7. A ........................ database consists of a set of tables containing either values of entity
attributes, or values of attributes from entity relationships.
8. A ........................ database is a set of records representing transactions, each with a time
stamp, an identifier and a set of items.
9. ........................ databases are databases that, in addition to usual data, store geographical
information like maps, and global or regional positioning.

10. ........................ databases contain time related data such stock market data or logged activities.
11. Data characterization is a summarization of general features of objects in a target class,
and produces what is called ........................

12. ........................ is the discovery of what are commonly called association rules.
13. ........................ is the organization of data in given classes.

9.2 Applications of Data Mining

Data mining is a relatively new technology that has not fully matured. Despite this, there are a
number of industries that are already using it on a regular basis. Some of these organizations
include retail stores, hospitals, banks and insurance companies.

Many of these organizations are combining data mining with such things as statistics, pattern
recognition, and other important tools. Data mining can be used to find patterns and connections
that would otherwise be difficult to find. This technology is popular with many businesses
because it allows them to learn more about their customers and make smart marketing decisions.
There are a number of applications that data mining has. The first is called market segmentation.
With market segmentation, you will be able to find behaviors that are common among your
customers. You can look for patterns among customers that seem to purchase the same products
at the same time. Another application of data mining is called customer churn. Customer churn
will allow you to estimate which customers are the most likely to stop purchasing your products
or services and go to one of your competitors. In addition to this, a company can use data mining
to find out which purchases are the most likely to be fraudulent.

Example: By using data mining a retail store may be able to determine which products
are stolen the most. By finding out which products are stolen the most, steps can be taken to
protect those products and detect those who are stealing them.
While direct mail marketing is an older technique that has been used for many years, companies
who combine it with data mining can experience fantastic results.

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