2008 MiningAdaptivelyFrequentClosedU

• (Bifet et al., 2008) ⇒ Albert Bifet, and Ricard Gavaldà. (2008). “Mining Adaptively Frequent Closed Unlabeled Rooted Trees in Data Streams.” In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2008). doi:10.1145/1401890.1401900

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• http://scholar.google.com/scholar?q=%22Mining+adaptively+frequent+closed+unlabeled+rooted+trees+in+data+streams%22+2008
• http://portal.acm.org/citation.cfm?doid=1401890.1401900&preflayout=flat#citedby

Quotes

Abstract

Closed patterns are powerful representatives of frequent patterns, since they eliminate redundant information. We propose a new approach for mining closed unlabeled rooted trees adaptively from data streams that change over time. Our approach is based on an efficient representation of trees and a low complexity notion of relaxed closed trees, and leads to an on-line strategy and an adaptive sliding window technique for dealing with changes over time. More precisely, we first present a general methodology to identify closed patterns in a data stream, using Galois Lattice Theory. Using this methodology, we then develop three closed tree mining algorithms: an incremental one IncTreeNat, a sliding-window based one, WinTreeNat, and finally one that mines closed trees adaptively from data streams, AdaTreeNat. To the best of our knowledge this is the first work on mining frequent closed trees in streaming data varying with time. We give a first experimental evaluation of the proposed algorithms.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2008 MiningAdaptivelyFrequentClosedU	Albert Bifet Ricard Gavaldà			Mining Adaptively Frequent Closed Unlabeled Rooted Trees in Data Streams				10.1145/1401890.1401900