PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Mining adaptively frequent closed unlabeled rooted trees in data streams
Albert Bifet and Ricard Gavaldà
In: The 14th ACM SIGKDD Intl. Conference on Knowledge Discovery and Data Mining (KDD'08), 21-24 Aug 2008, Las Vegas, USA.


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 nally one that mines closed trees adaptively from data streams, Ada-TreeNat. 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.

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EPrint Type:Conference or Workshop Item (Talk)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Theory & Algorithms
ID Code:4502
Deposited By:Ricard Gavaldà
Deposited On:13 March 2009