Mining frequent closed graphs on evolving data streams

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Title

Mining frequent closed graphs on evolving data streams

Content partner

University of Waikato

Collection

ResearchCommons@Waikato

Description

Graph mining is a challenging task by itself, and even more so when processing data streams which evolve in real-time. Data stream mining faces hard constraints regarding time and space for processing, and also needs to provide for concept drift detection. In this paper we present a framework for studying graph pattern mining on time-varying streams. Three new methods for mining frequent closed subgraphs are presented. All methods work on coresets of closed subgraphs, compressed representatio...

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Format

Research paper

Research format

Conference item

Date created

2011

Creator

Bifet, Albert / Holmes, Geoffrey / Pfahringer, Bernhard / Gavaldà, Ricard

URL

https://hdl.handle.net/10289/8555

Related subjects

computer science / data streams / closed mining / graphs / concept drift / Machine learning

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