[an error occurred while processing this directive]



Book Contents

Ch. 10
Building Knowledge-Driven DSS and Mining Data

Chapter Contents
Previous Page
Next Page

Data Mining Process

Data mining and knowledge discovery attempt to identify predictive relationships and provide managers with descriptive information about the subject of a database. There are a number of prescribed data mining processes. To make the best use of data mining, you must first make a clear statement of your objectives. Researchers at IBM have described data mining as a three-phase process of data preparation, mining operations, and presentation. Analysts at the Gartner Group describes it as a five-stage process:

1. Select and prepare the data to be mined.

2. Qualify the data via cluster and feature analysis.

3. Select one or more data mining tools.

4. Apply the data mining tool.

5. Apply the knowledge discovered to the company's specific line of business to achieve a business goal (Gerber, 1996).

These processes are similar. The first step is to select and prepare the data to be mined. Some data mining software packages include data preparation tools that can handle at least some of the preparation that needs to be done to the data. The second step is qualifying the data using cluster and feature analysis software. This step takes some business knowledge about the question that one is trying to answer. This is the step where bias in the data should be detected and removed (IBM, 1998). In the third step an appropriate data mining tool is selected and used. Finally, the results are presented to decision-makers and if they are considered useful they should be used to help achieve business goals.

[an error occurred while processing this directive]