Proceedings of the 2015 2nd International Forum on Electrical Engineering and Automation (IFEEA 2015)

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Mining Maximal Frequent Itemsets over Sampling Databases

Authors
Haifeng Li
Corresponding Author
Haifeng Li
Available Online January 2016.
DOI
10.2991/ifeea-15.2016.6How to use a DOI?
Keywords
Maximal Frequent Itemset, Sampling, Data Mining.
Abstract

Maximal frequent itemset is an important representation of frequent itemset. This paper focuses on how to achieve the maximal frequent itemsets over databases by sampling technique. We use a tree-based data synopsis to maintain the frequent itemsets, based on which, an efficient algorithm SMFI is proposed. Our extensive experimental studies over a dataset show that sampling is effective when mining the maximal frequent itemsets.

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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Volume Title
Proceedings of the 2015 2nd International Forum on Electrical Engineering and Automation (IFEEA 2015)
Series
Advances in Engineering Research
Publication Date
January 2016
ISBN
10.2991/ifeea-15.2016.6
ISSN
2352-5401
DOI
10.2991/ifeea-15.2016.6How 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  - 2016/01
DA  - 2016/01
TI  - Mining Maximal Frequent Itemsets over Sampling Databases
BT  - Proceedings of the 2015 2nd International Forum on Electrical Engineering and Automation (IFEEA 2015)
PB  - Atlantis Press
SP  - 28
EP  - 31
SN  - 2352-5401
UR  - https://doi.org/10.2991/ifeea-15.2016.6
DO  - 10.2991/ifeea-15.2016.6
ID  - Li2016/01
ER  -