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

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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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  -