Feature Extracting of Business Data Streams with Concept-Drifting

Provided by: AICIT
Topic: Big Data
Format: PDF
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.

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