Title :
Attribute (feature) completion - the theory of attributes from data mining prospect
Author_Institution :
Dept. of Comput. Sci., San Jose State Univ., CA, USA
Abstract :
A "correct" selection of attributes (features) is vital in data mining. As a first step, this paper constructs all possible attributes of a given relation. The results are based on the observations that each relation is isomorphic to a unique abstract relation, called a canonical model. The complete set of attributes of the canonical model is, then, constructed. Any attribute of a relation can be interpreted (via isomorphism) from such a complete set.
Keywords :
data mining; data models; database theory; relational databases; very large databases; abstract relation; attribute completion; canonical model; data mining; data model; feature selection; isomorphism; large database; relational database; Artificial intelligence; Association rules; Computer science; Data mining; Data models; Mathematical model; Mathematics; Reflection; Spatial databases;
Conference_Titel :
Data Mining, 2002. ICDM 2003. Proceedings. 2002 IEEE International Conference on
Print_ISBN :
0-7695-1754-4
DOI :
10.1109/ICDM.2002.1183914
