Research issues in data stream association rule mining-Reference-Cited by-同舟云学术

Research issues in data stream association rule mining

Published:2006-03 Issue:1 Volume:35 Page:14-19
ISSN:0163-5808
Container-title:ACM SIGMOD Record
language:en
Short-container-title:SIGMOD Rec.

Author:

Jiang Nan¹,Gruenwald Le¹

Affiliation:

1. The University of Oklahoma, Norman

Abstract

There exist emerging applications of data streams that require association rule mining, such as network traffic monitoring and web click streams analysis. Different from data in traditional static databases, data streams typically arrive continuously in high speed with huge amount and changing data distribution. This raises new issues that need to be considered when developing association rule mining techniques for stream data. This paper discusses those issues and how they are addressed in the existing literature.

Publisher

Association for Computing Machinery (ACM)

Subject

Information Systems,Software

Link

https://dl.acm.org/doi/pdf/10.1145/1121995.1121998

Reference46 articles.

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2. {Agrawal 1994} Rakesh Agrawal Ramakrishnan Srikant; Fast Algorithms for Mining Association Rules; Int'l Conf. on Very Large Databases; September 1994. {Agrawal 1994} Rakesh Agrawal Ramakrishnan Srikant; Fast Algorithms for Mining Association Rules; Int'l Conf. on Very Large Databases; September 1994.

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5. {Chang 2003} Joong Hyuk Chang Won Suk Lee Aoying Zhou; Finding Recent Frequent Itemsets Adaptively over Online Data Streams; ACM SIGKDD Int'l Conf. on Knowledge Discovery and Data Mining; August 2003. 10.1145/956750.956807 {Chang 2003} Joong Hyuk Chang Won Suk Lee Aoying Zhou; Finding Recent Frequent Itemsets Adaptively over Online Data Streams; ACM SIGKDD Int'l Conf. on Knowledge Discovery and Data Mining; August 2003. 10.1145/956750.956807

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