Method of Concept-Drifting Feature Extracting in Data Streams Based on Granular Computing

Chun Hua Ju; Zhao Qian Shuai

doi:10.4028/www.scientific.net/AMM.50-51.934

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 50-51Method of Concept-Drifting Feature Extracting in...

Method of Concept-Drifting Feature Extracting in Data Streams Based on Granular Computing

Abstract:

Business data streams are dynamic and easy to drift, extract concept-drifting feature is one important work of data streams mining. This paper describes the characteristics and the concept drift of data streams, and constructs the formal concept description model of streaming data based on granular computing firstly. Then, the paper proposes the concept lattice pairs’ based concept relaxation-matching coincidence degree algorithm; the feature extraction method is also described. Finally, experiment and analysis are presented in order to explain and evaluate the method.

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Periodical:

Applied Mechanics and Materials (Volumes 50-51)

Pages:

934-938

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.50-51.934

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Online since:

February 2011

Authors:

Chun Hua Ju, Zhao Qian Shuai

Keywords:

Concept-Drifting, Data Stream, FCA, Feature Extraction, Granular Computing

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