Proceedings of the 2016 International Symposium on Advances in Electrical, Electronics and Computer Engineering

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A Review of the Maximal Frequent Itemset Mining Algorithms over Dynamically Changed Data

Authors
Haifeng Li
Corresponding Author
Haifeng Li
Available Online April 2016.
DOI
10.2991/isaeece-16.2016.67How to use a DOI?
Keywords
maximal frequent itemset, data mining, stream
Abstract

Maximal frequent itemset mining is a very important method in mining frequent itemsets, which will reduce the mining meory cost and supply a better understanding of the rules generated by the frequent itemsets. In this paper, we review the maximal frequent itemset mining algorithms over a stream, which is an unlimited and dynamically changed data.

Copyright
© 2016, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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<Previous Article In Volume
Volume Title
Proceedings of the 2016 International Symposium on Advances in Electrical, Electronics and Computer Engineering
Series
Advances in Engineering Research
Publication Date
April 2016
ISBN
10.2991/isaeece-16.2016.67
ISSN
2352-5401
DOI
10.2991/isaeece-16.2016.67How to use a DOI?
Copyright
© 2016, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

risenwbib
TY  - CONF
AU  - Haifeng Li
PY  - 2016/04
DA  - 2016/04
TI  - A Review of the Maximal Frequent Itemset Mining Algorithms over Dynamically Changed Data
BT  - Proceedings of the 2016 International Symposium on Advances in Electrical, Electronics and Computer Engineering
PB  - Atlantis Press
SP  - 346
EP  - 350
SN  - 2352-5401
UR  - https://doi.org/10.2991/isaeece-16.2016.67
DO  - 10.2991/isaeece-16.2016.67
ID  - Li2016/04
ER  -