Data Mining: An Overview from Database Perspective

Copyright [©] (1996) by IEEE. Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distrubuted for profit. To copy otherwise, to republish, to post on servers, or to redistribute to lists, requires prior specific permission and/or a fee.

Mining information and knowledge from large databases has been recognized by many researchers as a key research topic in database systems and machine learning, and by many industrial companies as an important area with an opportunity of major revenues. Researchers in many different fields have shown great interest in data mining. Several emerging applications in information providing services, such as data warehousing and on-line services over the Internet, also call for various data mining techniques to better understand user behavior, to improve the service provided, and to increase the business opportunities. In response to such a demand, this article is to provide a survey, from a database researcher's point of view, on the data mining techniques developed recently. A classification of the availalbe data mining techniques is provided and a comparative study of such techniques of presented.

By: Ming-Syan Chen, Jiawei Han and Philip S. Yu

Published in: IEEE Transactions on Knowledge and Data Engineering, volume 8, (no 6), pages 866-83 in 1996

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