DocumentCode :
1279135
Title :
Data mining: an overview from a database perspective
Author :
Chen, Ming-Syan ; Han, Jiawei ; Yu, Philip S.
Author_Institution :
Dept. of Electr. Eng., Nat. Taiwan Univ., Taipei, Taiwan
Volume :
8
Issue :
6
fYear :
1996
fDate :
12/1/1996 12:00:00 AM
Firstpage :
866
Lastpage :
883
Abstract :
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 online services over the Internet, also call for various data mining techniques to better understand user behavior, to improve the service provided and to increase business opportunities. In response to such a demand, this article provides a survey, from a database researcher´s point of view, on the data mining techniques developed recently. A classification of the available data mining techniques is provided and a comparative study of such techniques is presented
Keywords :
Internet; deductive databases; generalisation (artificial intelligence); information services; knowledge acquisition; learning (artificial intelligence); pattern matching; reviews; very large databases; Internet; association rules; business opportunities; classification; comparative study; data characterization; data clustering; data cubes; data generalization; data mining; data warehousing; information-providing services; knowledge discovery; large databases; machine learning; multiple-dimensional databases; online services; overview; pattern matching algorithms; user behavior; Business; Data engineering; Data mining; Engineering management; Learning; Power engineering and energy; Power system management; Relational databases; Spatial databases; US Government;
fLanguage :
English
Journal_Title :
Knowledge and Data Engineering, IEEE Transactions on
Publisher :
ieee
ISSN :
1041-4347
Type :
jour
DOI :
10.1109/69.553155
Filename :
553155
Link To Document :
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