A Mining Algorithm of Maximal Frequent Itemsets Based on M-Bisearch

DOI

10.2991/snce-18.2018.207How to use a DOI?

Keywords

Data mining; Association rules; Frequent itemsets; Machine learning

Abstract

The core theory of big data analysis is data mining. Association rule mining algorithm is an important branch of data mining. It contains two steps: generation of frequent itemsets and generation of association rules. The algorithm overhead in the generation of frequent itemsets is very high. Starting from the nature of the maximal frequent itemsets, the idea of M-Bisearch is used on the basis of changing the data storage structure. The storage space is compressed to reduce the number of scans and reduce the computational overhead of support, so as to achieve the purpose of improving algorithm execution efficiency. Experiments show that the improved algorithm has obvious advantages when dealing with frequent itemsets mining in long-term mode.

Copyright

© 2018, 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  - Meilin Zeng
PY  - 2018/05
DA  - 2018/05
TI  - A Mining Algorithm of Maximal Frequent Itemsets Based on M-Bisearch
BT  - Proceedings of the 8th International Conference on Social Network, Communication and Education (SNCE 2018)
PB  - Atlantis Press
SP  - 1007
EP  - 1012
SN  - 2352-538X
UR  - https://doi.org/10.2991/snce-18.2018.207
DO  - 10.2991/snce-18.2018.207
ID  - Zeng2018/05
ER  -