Data Mining Techniques.

Conference PaperinACM SIGMOD Record 25(2):545 · June 1996with15 Reads
Impact Factor: 1.05 · DOI: 10.1145/235968.280351 · Source: DBLP
Conference: Proceedings of the 1996 ACM SIGMOD International Conference on Management of Data, Montreal, Quebec, Canada, June 4-6, 1996.

    Abstract

    Data mining, or knowledge discovery in databases, has been popularly recognized as an important research issue with broad applications. We provide a comprehensive survey, in database perspective, on the data mining techniques developed recently. Several major kinds of data mining methods, including generalization, characterization, classification, clustering, association, evolution, pattern matching, data visualization, and meta-rule guided mining, will be reviewed. Techniques for mining knowledge in different kinds of databases, including relational, transaction, object-oriented, spatial, and active databases, as well as global information systems, will be examined. Potential data mining applications and some research issues will also be discussed.