2011 MiningFrequentClosedGraphsonEvo

• (Bifet et al., 2011) ⇒ Albert Bifet, Geoff Holmes, Bernhard Pfahringer, and Ricard Gavaldà. (2011). “Mining Frequent Closed Graphs on Evolving Data Streams.” In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2011) Journal. ISBN:978-1-4503-0813-7 doi:10.1145/2020408.2020501

Subject Headings:

Cited By

• http://scholar.google.com/scholar?q=%222011%22+Mining+Frequent+Closed+Graphs+on+Evolving+Data+Streams
• http://dl.acm.org/citation.cfm?id=2020408.2020501&preflayout=flat#citedby

Quotes

Author Keywords

• Algorithms; closed mining; concept drift; data mining; data streams; graphs

Abstract

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 representations of graph sets, and maintain these sets in a batch-incremental manner, but use different approaches to address potential concept drift. An evaluation study on datasets comprising up to four million graphs explores the strength and limitations of the proposed methods. To the best of our knowledge this is the first work on mining frequent closed subgraphs in non-stationary data streams.

References

;

	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2011 MiningFrequentClosedGraphsonEvo	Albert Bifet Geoff Holmes Bernhard Pfahringer Ricard Gavaldà			Mining Frequent Closed Graphs on Evolving Data Streams				10.1145/2020408.2020501		2011