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Erschienen in:
2012 | OriginalPaper | Buchkapitel
Improved Counter Based Algorithms for Frequent Pairs Mining in Transactional Data Streams
verfasst von : Konstantin Kutzkov
Erschienen in: Machine Learning and Knowledge Discovery in Databases
Verlag: Springer Berlin Heidelberg
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A straightforward approach to frequent pairs mining in transactional streams is to generate all pairs occurring in transactions and apply a frequent items mining algorithm to the resulting stream. The well-known counter based algorithms
Frequent
and
Space-Saving
are known to achieve a very good approximation when the frequencies of the items in the stream adhere to a skewed distribution.
Motivated by observations on real datasets, we present a general technique for applying
Frequent
and
Space-Saving
to transactional data streams for the case when the transactions considerably vary in their lengths. Despite of its simplicity, we show through extensive experiments that our approach is considerably more efficient and precise than the naïve application of
Frequent
and
Space-Saving
.