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2017 | OriginalPaper | Chapter

Mining Strongly Closed Itemsets from Data Streams

Authors : Daniel Trabold, Tamás Horváth

Published in: Discovery Science

Publisher: Springer International Publishing

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Abstract

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.

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Footnotes
1
Due to space limitations we omit frequency constraints in this short version.
 
2
We note that Hoeffding’s inequality applies to samples without replacement as well [5]. A tighter bound can be derived from Serfling’s inequality [12]. The improvement becomes however marginal with increasing data stream length.
 
4
We are going to present further practical applications (e.g., computer aided product configuration) in the long version of this paper.
 
Literature
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2.
Boley, M., Horváth, T., Wrobel, S.: Efficient discovery of interesting patterns based on strong closedness. Stat. Anal. Data Mining 2(5–6), 346–360 (2009)MathSciNetCrossRef
3.
Gama, J., Žliobaitė, I., Bifet, A., Pechenizkiy, M., Bouchachia, A.: A survey on concept drift adaptation. ACM Comput. Surv. 46(4), 44:1–44:37 (2014)CrossRefMATH
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Lichman, M.: UCI machine learning repository (2013)
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Manku, G.S., Motwani, R.: Approximate frequency counts over data streams. In: Proceedings of the 28th International Conference on Very Large Data Bases (VLDB), pp. 346–357. VLDB Endowment (2002)
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Pasquier, N., Bastide, Y., Taouil, R., Lakhal, L.: Efficient mining of association rules using closed itemset lattices. Inf. Syst. 24(1), 25–46 (1999)CrossRefMATH
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Yen, S.J., Wu, C.W., Lee, Y.S., Tseng, V.S., Hsieh, C.H.: A fast algorithm for mining frequent closed itemsets over stream sliding window. In: IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), pp. 996–1002 (2011)
Metadata
Title
Mining Strongly Closed Itemsets from Data Streams
Authors
Daniel Trabold
Tamás Horváth
Copyright Year
2017
Book
Discovery Science

Print ISBN: 978-3-319-67785-9

Electronic ISBN: 978-3-319-67786-6

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-67786-6_18

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