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Mining strongly closed itemsets from data streams

: Trabold, D.; Horváth, T.


Yamamoto, A.:
Discovery science. 20th International Conference, DS 2017 : Kyoto, Japan, October 15-17, 2017, Proceedings
Cham: Springer International Publishing, 2017 (Lecture Notes in Computer Science 10558)
ISBN: 978-3-319-67785-9 (Print)
ISBN: 978-3-319-67786-6 (Online)
ISBN: 3-319-67785-3
International Conference on Discovery Science (DS) <20, 2017, Kyoto>
Conference Paper
Fraunhofer IAIS ()

We consider the problem of mining strongly closed itemsets from transactional data streams. Compactness and stability against changes in the input are two characteristic features of this kind of itemsets that make them appealing for different applications. Utilizing their algebraic and algorithmic properties, we propose an algorithm based on reservoir sampling for approximating this type of itemsets in the landmark streaming setting, prove its correctness, and show empirically that it yields a considerable speed-up over a straightforward naive algorithm without any significant loss in precision and recall. As a motivating application, we experimentally demonstrate the suitability of strongly closed itemsets to concept drift detection in transactional data streams.