Database Reference
In-Depth Information
can now begin not only keep track of store-wide purchasing trends, but individual purchasing
trends as well. The store can target market to you by sending mailers with coupons for products
you tend to purchase most frequently.
Now let's take it one step further. Remember, if you can, what types of information you provided
when you filled out the form to receive your frequent shopper card. You probably indicated your
address, date of birth (or at least birth year), whether you're male or female, and perhaps the size of
your family, annual household income range, or other such information. Think about the range of
possibilities now open to your grocery store as they analyze that vast amount of data they collect at
the cash register each day:
Using ZIP codes, the store can locate the areas of greatest customer density, perhaps
aiding their decision about the construction location for their next store.
Using information regarding customer gender, the store may be able to tailor marketing
displays or promotions to the preferences of male or female customers.
With age information, the store can avoid mailing coupons for baby food to elderly
customers, or promotions for feminine hygiene products to households with a single
male occupant.
These are only a few the many examples of potential uses for data mining. Perhaps as you read
through this introduction, some other potential uses for data mining came to your mind. You may
have also wondered how ethical some of these applications might be. This text has been designed
to help you understand not only the possibilities brought about through data mining, but also the
techniques involved in making those possibilities a reality while accepting the responsibility that
accompanies the collection and use of such vast amounts of personal information.
LEARNING OBJECTIVES
After completing the reading and exercises in this chapter, you should be able to:
Define the discipline of Data Mining
List and define various types of data
List and define various sources of data
Explain the fundamental differences between databases, data warehouses and data sets
