DocumentCode
384282
Title
Discrete approach for automatic knowledge extraction from precedent large-scale data, and classification
Author
Ryazanov, Vladimir V. ; Vorontchikhin, Victor A.
Author_Institution
Comput. Center, Acad. of Sci., Moscow, Russia
Volume
2
fYear
2002
fDate
2002
Firstpage
188
Abstract
The proposed method for automatic knowledge extraction from large-scale data is based on the idea of analysing neighborhoods of "supporting" objects and construction of data covered by sets of hyper parallelepipeds. A simple procedure to choose the supporting objects is applied. Knowledge extraction (logical regularities search) is based on the solution of special discrete linear optimization tasks associated with supporting objects. Two practical tasks are considered for method illustration.
Keywords
data mining; optimisation; pattern classification; search problems; automatic knowledge extraction; data analysis; discrete linear optimization; hyper parallelepipeds; large-scale data; logical regularity search; pattern classification; Data mining; Equations; Information analysis; Large-scale systems; Polynomials; Training data; Vectors;
fLanguage
English
Publisher
ieee
Conference_Titel
Pattern Recognition, 2002. Proceedings. 16th International Conference on
ISSN
1051-4651
Print_ISBN
0-7695-1695-X
Type
conf
DOI
10.1109/ICPR.2002.1048269
Filename
1048269
Link To Document