Proceedings of the 2016 5th International Conference on Advanced Materials and Computer Science

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A Theoretical Comparison of Two Maximal Frequent Itemset Mining Algorithms

Authors
Haifeng Li
Corresponding Author
Haifeng Li
Available Online June 2016.
DOI
10.2991/icamcs-16.2016.77How to use a DOI?
Keywords
maximal frequent itemset, data mining
Abstract

Frequent pattern mining is one of the most important methods in data mining. The maximal frequent patterns are the effective condensed representation of frequent patterns; thus, they can supply a deep understanding of data for users with less storage cost. This paper introduces the concept and characteristics of maximal frequent patterns and compares two maximal frequent itemset mining algorithms in detail.

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 2016 5th International Conference on Advanced Materials and Computer Science
Series
Advances in Engineering Research
Publication Date
June 2016
ISBN
978-94-6252-189-6
ISSN
2352-5401
DOI
10.2991/icamcs-16.2016.77How 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  - 2016/06
DA  - 2016/06
TI  - A Theoretical Comparison of Two Maximal Frequent Itemset Mining Algorithms
BT  - Proceedings of the 2016 5th International Conference on Advanced Materials and Computer Science
PB  - Atlantis Press
SP  - 363
EP  - 366
SN  - 2352-5401
UR  - https://doi.org/10.2991/icamcs-16.2016.77
DO  - 10.2991/icamcs-16.2016.77
ID  - Li2016/06
ER  -