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AI Magazine Volume 17 Number 3 (1996) (© AAAI)
Articles

From Data Mining to Knowledge Discovery in Databases
Usama Fayyad, Gregory Piatetsky-Shapiro, and Padhraic Smyth

s Data mining and knowledge discovery in databases have been attracting a significant amount of research, industry, and media attention of late. What is all the excitement about? This article provides an overview of thisemerging field, clarifying how data mining and knowledge discovery in databases are related both to each other and to related fields, such as machine learning, statistics, and databases. The article mentions particular real-world applications, specific data-mining techniques, challenges involved in real-world applications of knowledge discovery, and current and future research directions in the field.A

cross a wide variety of fields, data are being collected and accumulated at a dramatic pace. There is an urgent need for a new generation of computational theories and tools to assist humans in extracting useful information (knowledge) from the rapidly growing volumes of digital data. These theories and tools are the subject of the emerging field of knowledge discovery in databases (KDD). Atan abstract level, the KDD field is concerned with the development of methods and techniques for making sense of data. The basic problem addressed by the KDD process is one of mapping low-level data (which are typically too voluminous to understand and digest easily) into other forms that might be more compact (for example, a short report), more abstract (for example, a descriptive approximation ormodel of the process that generated the data), or more useful (for example, a predictive model for estimating the value of future cases). At the core of the process is the application of specific data-mining methods for pattern discovery and extraction.1

This article begins by discussing the historical context of KDD and data mining and their intersection with other related fields. A briefsummary of recent KDD real-world applications is provided. Definitions of KDD and data mining are provided, and the general multistep KDD process is outlined. This multistep process has the application of data-mining algorithms as one particular step in the process. The data-mining step is discussed in more detail in the context of specific data-mining algorithms and their application. Real-worldpractical application issues are also outlined. Finally, the article enumerates challenges for future research and development and in particular discusses potential opportunities for AI technology in KDD systems.

Why Do We Need KDD?
The traditional method of turning data into knowledge relies on manual analysis and interpretation. For example, in the health-care industry, it is common for specialiststo periodically analyze current trends and changes in health-care data, say, on a quarterly basis. The specialists then provide a report detailing the analysis to the sponsoring health-care organization; this report becomes the basis for future decision making and planning for health-care management. In a totally different type of application, planetary geologists sift through remotely sensedimages of planets and asteroids, carefully locating and cataloging such geologic objects of interest as impact craters. Be it science, marketing, finance, health care, retail, or any other field, the classical approach to data analysis relies fundamentally on one or more analysts becoming

Copyright © 1996, American Association for Artificial Intelligence. All rights reserved. 0738-4602-1996 / $2.00FALL 1996

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Articles

There is an urgent need for a new generation of computational theories and tools to assist humans in extracting useful information (knowledge) from the rapidly growing volumes of digital data.

intimately familiar with the data and serving as an interface between the data and the users and products. For these (and many other) applications, this form of manual...
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