## Abstract

While there has been a lot of work on finding frequent itemsets 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 itemsets:

Any algorithm that (even only approximately and with a chance of error) finds the most frequent $k$-itemset must use space $\Omega(\min\{mb,n^k,(mb/\varphi)^k\})$ bits, where $mb$ is the number of items in the stream so far, $n$ is the number of distinct items and $\varphi$ 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 {\em improves\/} with the length of the stream for any fixed support threshold.

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 itemsets:

Any algorithm that (even only approximately and with a chance of error) finds the most frequent $k$-itemset must use space $\Omega(\min\{mb,n^k,(mb/\varphi)^k\})$ bits, where $mb$ is the number of items in the stream so far, $n$ is the number of distinct items and $\varphi$ 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 {\em improves\/} with the length of the stream for any fixed support threshold.

Original language | English |
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Title of host publication | KDCloud 2010 : Proceedings of the International Workshop on Knowledge Discovery Using Cloud and Distributed Computing Platforms |

Number of pages | 8 |

Publisher | IEEE Computer Society Press |

Publication date | 14 Dec 2010 |

Publication status | Published - 14 Dec 2010 |

Event | IEEE International Conference on Data Mining - Duration: 2 Jul 2010 → … Conference number: 9 |

### Conference

Conference | IEEE International Conference on Data Mining |
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Number | 9 |

Period | 02/07/2010 → … |