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

Abstract:

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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.

Info:

Periodical:

Edited by:

Shaobo Zhong, Yimin Cheng and Xilong Qu

Pages:

934-938

DOI:

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

Citation:

C. H. Ju and Z. Q. Shuai, "Method of Concept-Drifting Feature Extracting in Data Streams Based on Granular Computing", Applied Mechanics and Materials, Vols. 50-51, pp. 934-938, 2011

Online since:

February 2011

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$35.00

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