Feature Extracting of Business Data Streams with Concept-drifting
Chunhua Ju, Shuai Zhaoqian -, Chen Tinggui - · Journal of Convergence Information Technology · 2011
Business data streams are dynamic and easy to drift, thus extracting concept-drifting feature is one important work of data streams mining. This paper describes the characteristics and the concept drift of data streams, proposes work flow of concept formal analysis and the formal concept description model of streaming data based on granular computing. Concept-drifting in business data streams is actually decided by the changes upon the extension of the concept. Then, the paper describes concept coincidence, including coincidence on extent, coincidence on intent and coincidence on concept. Because concept-lattices can be expressed in terms of the granulated data streams, we analyze concept lattice pairs’ coincidence instead of continued and non-formal streaming data. And 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.