Mining frequent itemsets from data streams with a time-sensitive sliding window

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Mining frequent itemsets from data streams with a time-sensitive sliding window (2005)

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by Chih-hsiang Lin , Ding-ying Chiu , Yi-hung Wu

Venue:	In SDM
Citations:	23 - 0 self

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@INPROCEEDINGS{Lin05miningfrequent,
    author = {Chih-hsiang Lin and Ding-ying Chiu and Yi-hung Wu},
    title = {Mining frequent itemsets from data streams with a time-sensitive sliding window},
    booktitle = {In SDM},
    year = {2005}
}

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Abstract

Mining frequent itemsets has been widely studied over the last decade. Past research focuses on mining frequent itemsets from static databases. In many of the new applications, data flow through the Internet or sensor networks. It is challenging to extend the mining techniques to such a dynamic environment. The main challenges include a quick response to the continuous request, a compact summary of the data stream, and a mechanism that adapts to the limited resources. In this paper, we develop a novel approach for mining frequent itemsets from data streams based on a time-sensitive sliding window model. Our approach consists of a storage structure that captures all possible frequent itemsets and a table providing approximate counts of the expired data items, whose size can be adjusted by the available storage space. Experiment results show that in our approach both the execution time and the storage space remain small under various parameter settings. In addition, our approach guarantees no false alarm or no false dismissal to the results yielded. 1

Keyphrases

frequent itemsets data stream time-sensitive sliding window approximate count storage structure static database last decade novel approach sensor network continuous request experiment result main challenge storage space false alarm possible frequent itemsets time-sensitive sliding window model mining technique various parameter setting compact summary execution time dynamic environment past research available storage space quick response new application data item false dismissal limited resource

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