## Abstract

We propose an online partial counting algorithm based on statistical inference that approximates itemset frequencies from data streams. The space complexity of our algorithm is proportional to the number of frequent itemsets in the stream at any time. Furthermore, the longer an itemset is frequent the closer is the approximation to its frequency, implying that the results become more precise as the stream evolves. We empirically compare our approach in terms of correctness and memory footprint to CARMA and Lossy Counting. Though our algorithm outperforms only CARMA in correctness, it requires much less space than both of these algorithms providing an alternative to Lossy Counting when the memory available is limited.

Original language | English |
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Title of host publication | Proceedings of the LWA 2015 Workshops: KDML, FGWM, IR, FGDB |

Subtitle of host publication | Trier, Germany, October 7-9, 2015. |

Editors | Ralph Bergmann, Sebastian Görg, Gilbert Müller |

Publisher | Rheinisch-Westfaelische Technische Hochschule Aachen |

Pages | 93-104 |

Number of pages | 12 |

Volume | 1458 |

ISBN (Electronic) | 007414588 |

Publication status | Published - 2015 |

Externally published | Yes |

Event | Workshop on Knowledge Discovery, Data Mining and Machine Learning 2015 - Trier, Germany Duration: 7 Oct 2015 → 9 Oct 2016 http://lwa2015.wi2.uni-trier.de/call-for-papers-kdml-2015/ |

### Publication series

Name | CEUR Workshop Proceedings |
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Publisher | Rheinisch-Westfaelische Technische Hochschule Aachen * Lehrstuhl Informatik V |

ISSN (Print) | 1613-0073 |

### Conference

Conference | Workshop on Knowledge Discovery, Data Mining and Machine Learning 2015 |
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Abbreviated title | KDML |

Country | Germany |

City | Trier |

Period | 7/10/15 → 9/10/16 |

Internet address |