Conference Paper

Data Mining Techniques.

DOI: 10.1145/235968.280351 Conference: Proceedings of the 1996 ACM SIGMOD International Conference on Management of Data, Montreal, Quebec, Canada, June 4-6, 1996.
Source: DBLP

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.

0 Bookmarks
 · 
126 Views
  • [Show abstract] [Hide abstract]
    ABSTRACT: Clustering is the process of grouping physical or abstract objects into classes of similar objects. These groups of similar objects are called clusters. Objects in one cluster are very similar to other objects in that particular cluster but very dissimilar when compared to objects in other clusters. Portraying data by fewer clusters necessarily loses certain fine details (akin to lossy data compression), but achieves simplification. It portrays many data objects by few clusters, and hence, it models data by its clusters. Clustering Analysis is broadly used in many applications such as market research, pattern recognition, data analysis, and image processing. In this paper we give a broad description of different clustering algorithms and methods that exist in data mining.
    ELSEVIER conference. 08/2014;
  • [Show abstract] [Hide abstract]
    ABSTRACT: An abstract is not available.
    01/1997;
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Various studies to increase customer satisfaction of a web based system are performed actively. Also in recent days an interest about the personalization that supporting a order type service on customer's viewpoint was raised. So the studies supporting the personalization is required in a web-based marketing system. In this study, we designed an intelligent recommendation system which supporting one to one web marketing using cross selling. The proposed system used an intelligent data mining method as a concurrent cross selling and a sequential cross selling. Also, In experiment on the prototype, we show a proposed system was usable in an practical system applying the mining result.
    The KIPS Transactions PartD 12/2004;