IBM Switch 15 Bedienungsanleitung PDF herunterladen (Seite 40)

Chapter 4

Typically, you will use these facilities to identify a promising set of attributes in the data. These

attribut es ca

n then be fed to the modeling techniques, which will attempt to identify underlying

rules and relationships.

Typical Applications

Typical applications of data mining techniques include the following:

Direct mail.

Determine which demographic groups have the highest resp onse rate. Use this

informa tion to maximize the response to future mailings.

Credit scoring.

Use an individual’s credit histo r y to make credit decisions.

Human resources.

Understand past hiring p r actices and cr eate decision rules to str eamline the

hiring process.

Medical research.

Create decision rules that suggest appro priate procedures based on medical

evidence.

Market analysis.

Deter mine which variab les, such as geography, price, and custome r

characteristics, are a ssociated w ith sales.

Quality control.

Analyze data fr om product manufacturing and identify variables determining

product defects.

Policy studies.

Use survey data to formulate policy by applying dec ision rules to select the most

important variables.

Health care.

User surveys and clinical data can be combined to discover variables t hat contribute

to health.

Terminology

The terms attribute, ﬁeld, and variable refer to a single data item common to al l cases under

consideration. A collection of attribute values that refers to a speciﬁc case is called a record, an

example, or a case.

Assessing the Data

Data mining is not likely to be fruitful unless the data you want to use meets certain criteria. The

followi ng sections present some of the aspects of the data and its applicatio n that you should

consider.

Ensure that the data is available

This m ay see m obvious, but be aware that although data might be available, it may not be in a

form t ha t can be used easily. IBM® SPSS® Modeler can import data from databases (through

ODBC) or from ﬁles. The data, ho wever, might be held in some other fo r m on a machine that

cannot be direc tly accessed. It will need to be downloaded or dumped in a suitable for m before it

can be used. It might be scattered among different databases and s ources and need to be pull ed

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