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• W2116427863 abstract "While there has been a lot of work on finding frequent item sets in transaction data streams, none of these solve the problem of finding similar pairs according to standard similarity measures. This paper is a first attempt at dealing with this, arguably more important, problem. We start out with a negative result that also explains the lack of theoretical upper bounds on the space usage of data mining algorithms for finding frequent item sets: Any algorithm that (even only approximately and with a chance of error) finds the most frequent k-item set must use space Ω(min{mb, n <sup xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>k</sup> , (mb/φ) <sup xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>k</sup> }) bits, where mb is the number of items in the stream so far, n is the number of distinct items and phi is a support threshold. To achieve any non-trivial space upper bound we must thus abandon a worst-case assumption on the data stream. We work under the model that the transactions come in random order, and show that surprisingly, not only is small-space similarity mining possible for the most common similarity measures, but the mining accuracy improves with the length of the stream for any fixed support threshold." @default.
• W2116427863 created "2016-06-24" @default.
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• W2116427863 date "2010-12-01" @default.
• W2116427863 modified "2023-09-24" @default.
• W2116427863 title "On Finding Similar Items in a Stream of Transactions" @default.
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• W2116427863 doi "https://doi.org/10.1109/icdmw.2010.152" @default.
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