Partitioning large data to scale up lattice-based algorithm

Author

Fu, Huaiguo ; Nguifo, Engelbert Mephu

Author_Institution

CRIL-CNRS, Univ. d´´Artois, Lens, France

fYear

2003

fDate

3-5 Nov. 2003

Firstpage

537

Lastpage

541

Abstract

Concept lattice is an effective tool and platform for data analysis and knowledge discovery such as classification or association rules mining. The lattice algorithm to build formal concepts and concept lattice plays an essential role in the application of concept lattice. We propose a new efficient scalable lattice-based algorithm: ScalingNextClosure to decompose the search space of any huge data in some partitions, and then generate independently concepts (or closed itemsets) in each partition. The experimental results show the efficiency of this algorithm.

Keywords

artificial intelligence; data analysis; data mining; search problems; ScalingNextClosure; association rule mining; classification rule mining; concept lattice; data analysis; formal concepts; knowledge discovery; large data partitioning; lattice algorithm; lattice-based algorithm; search space decomposition; Artificial intelligence; Association rules; Data analysis; Data mining; Itemsets; Lattices; Lenses; Machine learning; Machine learning algorithms; Partitioning algorithms;

fLanguage

English

Publisher

ieee

Conference_Titel

Tools with Artificial Intelligence, 2003. Proceedings. 15th IEEE International Conference on

ISSN

1082-3409

Print_ISBN

0-7695-2038-3

Type

conf

DOI

10.1109/TAI.2003.1250237

Filename

1250237