Mining frequent itemsets from streaming transaction data using genetic algorithms

Mining frequent itemsets from streaming transaction data using genetic algorithms

Abstract This paper presents a study of mining frequent itemsets from streaming data in the presence of concept drift. Streaming data, being volatile in nature, is particularly challenging to mine. An approach using genetic algorithms is presented, and various relationships between concept drift, sl...

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Bibliographic Details
Main Authors: Sikha Bagui, Patrick Stanley
Format: Article
Language:English
Published: SpringerOpen 2020-07-01
Series:Journal of Big Data
Subjects:
Frequent itemsets
Streaming data
Concept drift
Sliding window
Association rules mining
Genetic algorithms
Online Access:http://link.springer.com/article/10.1186/s40537-020-00330-9

Internet

http://link.springer.com/article/10.1186/s40537-020-00330-9

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