A growing number of fields, in particular the fields of business and science, are turning to data mining to make sense of large volumes of data. Financial institutions, manufacturing companies, and government agencies are just a few of the types of organizations using data mining. Data mining is also being used to address a wide range of problems, such as managing financial portfolios, optimizing marketing campaigns, and identifying insurance fraud. The adoption of data mining techniques is driven by a combination of competitive pressure, the availability of large amounts of data, and ever increasing computing power. Organizations that apply it to critical operations achieve significant returns. The use of a process helps ensure that the results from data mining projects translate into actionable and profitable business decisions. The following chapter summarizes four steps necessary to complete a data mining project: (1) definition, (2) preparation, (3) analysis, and (4) deployment. The methods discussed in this book are reviewed within this context. This chapter concludes with an outline of the book's content and suggestions for further reading.
The first step in any data mining process is to define and plan the project. The following summarizes issues to consider when defining a project:
- Objectives: Articulating the overriding business or scientific objective of the data mining project is an important first step. Based on this ...
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