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2008
ACM

Mining adaptively frequent closed unlabeled rooted trees in data streams

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Mining adaptively frequent closed unlabeled rooted trees in data streams
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 algorit...
Albert Bifet, Ricard Gavaldà
Added 30 Nov 2009
Updated 30 Nov 2009
Type Conference
Year 2008
Where KDD
Authors Albert Bifet, Ricard Gavaldà
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