document: 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 algorithms .
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