Mining Data Streams for Frequent Sequences Extraction

Alice Marascu 1 Florent Masseglia 1
1 AxIS - Usage-centered design, analysis and improvement of information systems
CRISAM - Inria Sophia Antipolis - Méditerranée , Inria Paris-Rocquencourt
Abstract : In recent years, emerging applications introduced new constraints for data mining methods. These constraints are particularly linked to new kinds of data that can be considered as complex data. One typical kind of such data is known as data streams. In a data stream processing, memory usage is restricted, new elements are generated continuously and have to be considered as fast as possible, no blocking operator can be performed and the data can be examined only once. At this time and to the best of our knowledge, no method has been proposed for mining sequential patterns in data streams. We argue that the main reason is the combinatory phenomenon related to sequential pattern mining. In this paper, we propose an algorithm based on sequences alignment for mining approximate sequential patterns in Web usage data streams. To meet the constraint of one scan, a greedy clustering algorithm associated to an alignment method are proposed. We will show that our proposal is able to extract relevant sequences with very low thresholds.
Type de document :
Communication dans un congrès
IEEE first Workshop on Mining Complex Data (MCD'05). Held in conjunction with ICDM'05, Nov 2005, Houston, United States. 2005
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https://hal.inria.fr/inria-00461876
Contributeur : Alice-Maria Marascu <>
Soumis le : samedi 6 mars 2010 - 15:32:55
Dernière modification le : mercredi 21 novembre 2018 - 19:48:04

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  • HAL Id : inria-00461876, version 1

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Alice Marascu, Florent Masseglia. Mining Data Streams for Frequent Sequences Extraction. IEEE first Workshop on Mining Complex Data (MCD'05). Held in conjunction with ICDM'05, Nov 2005, Houston, United States. 2005. 〈inria-00461876〉

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