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Mining Frequent Itemsets in a Stream
Omaha, Nebraska, USA October 28-October 31
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICDM.2007.662007 Seventh IEEE International Confe ...
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Toon Calders, Nele Dexters, Bart Goethals, "Mining Frequent Itemsets in a Stream," Data Mining, IEEE International Conference on, pp. 83-92, 2007 Seventh IEEE International Conference on Data Mining, 2007.
 
 
BibTex
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@article{ 10.1109/ICDM.2007.66,
author = {Toon Calders and Nele Dexters and Bart Goethals},
title = {Mining Frequent Itemsets in a Stream},
journal ={Data Mining, IEEE International Conference on},
volume = {0},
year = {2007},
issn = {1550-4786},
pages = {83-92},
doi = {http://doi.ieeecomputersociety.org/10.1109/ICDM.2007.66},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - CONF
JO - Data Mining, IEEE International Conference on
TI - Mining Frequent Itemsets in a Stream
SN - 1550-4786
SP83
EP92
A1 - Toon Calders,
A1 - Nele Dexters,
A1 - Bart Goethals,
PY - 2007
VL - 0
JA - Data Mining, IEEE International Conference on
ER -
 
We study the problem of finding frequent itemsets in a continuous stream of transactions. The current frequency of an itemset in a stream is defined as its maximal frequency over all possible windows in the stream from any point in the past until the current state that satisfy a minimal length constraint. Properties of this new measure are studied and an incremental algorithm that allows, at any time, to immediately produce the current frequencies of all frequent itemsets is proposed. Experimental and theoretical analysis show that the space requirements for the algorithm are extremely small for many realistic data distributions.
Citation:
Toon Calders, Nele Dexters, Bart Goethals, "Mining Frequent Itemsets in a Stream," icdm, pp.83-92, 2007 Seventh IEEE International Conference on Data Mining, 2007
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