A cellular-based evolutionary approach for the extraction of emerging patterns in massive data streams
作者：
Highlights：
• A cellular-based evolutionary fuzzy system for the extraction of emerging patterns in high-speed, massive data streams is proposed.
• Smart triggering of the learning method which updates the model only when required.
• A reinitialisation and filtering strategy for reducing the extraction of redundant patterns is also defined.
• The quality of knowledge extracted outperforms state-of-the-art methods.
• The proposed method is able to process batches of data up to 750,000 instances.
摘要
•A cellular-based evolutionary fuzzy system for the extraction of emerging patterns in high-speed, massive data streams is proposed.•Smart triggering of the learning method which updates the model only when required.•A reinitialisation and filtering strategy for reducing the extraction of redundant patterns is also defined.•The quality of knowledge extracted outperforms state-of-the-art methods.•The proposed method is able to process batches of data up to 750,000 instances.
论文关键词：Big data,Data stream mining,Emerging pattern mining,Fuzzy logic,Evolutionary algorithms
论文评审过程：Received 14 January 2021, Revised 7 May 2021, Accepted 9 June 2021, Available online 23 June 2021, Version of Record 23 June 2021.
论文官网地址：https://doi.org/10.1016/j.eswa.2021.115419
