A hierarchical distributed data mining architecture

Author

Liu, Bin ; Cao, Shu-gui ; Li, Qing-chun ; Li, Qi

Author_Institution

Dept. of Comput. Sci. & Technol., Tsinghua Univ., Beijing, China

Volume

1

fYear

2011

fDate

10-13 July 2011

Firstpage

40

Lastpage

44

Abstract

Current distributed data mining (DDM) systems popularly assume distributed data sources as partitions of a virtual data table and separately mine them. In fact, when there is essential difference among data sources, the assumption will fail and DDM result quality will also be damaged. For this issue, a hierarchical DDM architecture is proposed by grouping data sources according to their similarity. Ontology technology is adopted to depict the essential content of data sources and measure their similarity.

Keywords

collections of physical data; data mining; distributed processing; DDM systems; current distributed data mining; distributed data sources; hierarchical distributed data mining architecture; ontology technology; virtual data table; Computer architecture; Cybernetics; Data mining; Data models; Delta modulation; Machine learning; Ontologies; Distributed data mining; hierarchical architecture; ontology;

fLanguage

English

Publisher

ieee

Conference_Titel

Machine Learning and Cybernetics (ICMLC), 2011 International Conference on

Conference_Location

Guilin

ISSN

2160-133X

Print_ISBN

978-1-4577-0305-8

Type

conf

DOI

10.1109/ICMLC.2011.6016720

Filename

6016720