Databases Reference
In-Depth Information
cess guide, but that would probably be true for any reasonable descrip-
tion of the data mining process.
Topic Area Understanding
Back in the last section, before Data Miner 1 begins to mine data he or she would
first need to understand the data and then the problem space. Problem type and
scope vary dramatically by subject area, from retail business, sales forecasts, and
inventory management to logistics. Outside the realm of business there are data-
mining-relevant science questions such as, "Some star has luminosity L, radiates
brightly in the ultraviolet spectrum, and appears to have a small surface area. How
hot is it likely to be?" If we have a reasonably sized database from which to train
initially — more on what that means later — predictive data mining could uncover
that the hotter the star, the shorter the wavelength peak in the star's spectrum and
that the hottest stars peak in the ultraviolet area of the spectrum. You do need to
have an idea about the topic or subject area and the problem you want to solve in
order to identify nonintuitive relationships and patterns using data mining as a tool.
To accomplish the goal of your data mining project, you must under-
stand what business you're in; moreover, what success means for your
business in quantitative terms. By "understand the business" you have
to sometimes ask the hard questions like does your company sell sugar
water with flavoring? Or does it really sell an imagined lifestyle of fun,
action, and perpetual happiness through the marketing of sugar water?
What metrics can be used to measure gradients of success? For ex-
ample, volumes of sales, market share ownership, or customer feedback
scores? You as the person to mine your company's data or as a data
mining consultant at a customer's site need to know as much informa-
tion as possible about the customer and what they are trying to achieve
so that you can interpret the results from data mining and make good
recommendations. The bottom line is the company wants to improve
profits earned. That can be accomplished by targeting the best sellers,
adding value for customers (in the form of making suggestions based on
customer usage patterns or detecting frauds against customer's credit
cards) and loss reduction (by identifying processes which drain the busi-
ness of funds unnecessarily).
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