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Mining frequent itemsets in a stream

Authors :
Toon Calders
Joris J. M. Gillis
Nele Dexters
Bart Goethals
Calders, Toon
Dexters, Nele
GILLIS, Joris
GOETHALS, Bart
Source :
Information Systems, 39(1), 233-255. Elsevier, Information systems
Publication Year :
2014
Publisher :
Elsevier BV, 2014.

Abstract

Mining frequent itemsets in a datastream proves to be a difficult problem, as itemsets arrive in rapid succession and storing parts of the stream is typically impossible. Nonetheless, it has many useful applications; e.g., opinion and sentiment analysis from social networks. Current stream mining algorithms are based on approximations. In earlier work, mining frequent items in a stream under the max-frequency measure proved to be effective for items. In this paper, we extended our work from items to itemsets. Firstly, an optimized incremental algorithm for mining frequent itemsets in a stream is presented. The algorithm maintains a very compact summary of the stream for selected itemsets. Secondly, we show that further compacting the summary is non-trivial. Thirdly, we establish a connection between the size of a summary and results from number theory. Fourthly, we report results of extensive experimentation, both of synthetic and real-world datasets, showing the efficiency of the algorithm both in terms of time and space. (C) 2012 Elsevier Ltd. All rights reserved.

Details

ISSN :
03064379
Volume :
39
Database :
OpenAIRE
Journal :
Information Systems
Accession number :
edsair.doi.dedup.....7e8633f830f9c99afa3032cdfbcfdd3b
Full Text :
https://doi.org/10.1016/j.is.2012.01.005