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.
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.
- [Show abstract] [Hide abstract]
ABSTRACT: Efficient function representation is very important for speed and memory requirements of multiple-valued decomposers. This paper presents a new representation of multiple-valued relations (functions in particular), called multiple-valued cube diagram bundles (MVCDB). MVCDBs improve on rough partition representation by labeling their blocks with variable values and by representing blocks efficiently. The MVCDB representation is especially efficient for very strongly unspecified multiple-valued input, multiple-valued output functions and relations, typical for machine learning applicationsMultiple-Valued Logic, 1997. Proceedings., 1997 27th International Symposium on; 01/1997
Conference Paper: DVIZ: A System for Visualizing Data Mining.[Show abstract] [Hide abstract]
ABSTRACT: We introduce an interactive system which visualizes the knowledge in data mining processes, including attribute values, evolutionary attributes, associations of attributes, classifications and hierarchical concepts. The basic framework of knowledge visualization in data mining is discussed and the algorithms for visualizing different forms of knowledge are presented. The application of our initial prototype system, DVIZ, to Canada Education Statistics is described and some preliminary results presented.Methodologies for Knowledge Discovery and Data Mining, Third Pacific-Asia Conference, PAKDD-99, Beijing, China, April 26-28, 1999, Proceedings; 01/1999
Data provided are for informational purposes only. Although carefully collected, accuracy cannot be guaranteed. The impact factor represents a rough estimation of the journal's impact factor and does not reflect the actual current impact factor. Publisher conditions are provided by RoMEO. Differing provisions from the publisher's actual policy or licence agreement may be applicable.