Design and Implementation of an Improved Apriori Data Mining Algorithm

DOI

10.2991/snce-18.2018.58How to use a DOI?

Keywords

Data Mining; Association Rules; Frequent Itemsets; Apriori Algorithm

Abstract

The need to scan the database D many times when the Apriori algorithm is applied to a large database causes the I/O load overhead of the disk to increase. An improved Apriori algorithm is designed. After scanning the original database D for the first time, it generates a candidate transaction database . In the process of generating frequent itemsets, the candidate transaction database is scanned each time. Experiments show that if the K value is very large, the number of will be much less than that of the original database, which will solve the problem of I/O overload and reduce operation time, so as to achieve the purpose of optimizing Apriori algorithm.

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
AU  - Qiangqiang Xiong
AU  - Ke Li
PY  - 2018/05
DA  - 2018/05
TI  - Design and Implementation of an Improved Apriori Data Mining Algorithm
BT  - Proceedings of the 8th International Conference on Social Network, Communication and Education (SNCE 2018)
PB  - Atlantis Press
SP  - 291
EP  - 296
SN  - 2352-538X
UR  - https://doi.org/10.2991/snce-18.2018.58
DO  - 10.2991/snce-18.2018.58
ID  - Zeng2018/05
ER  -