The Comparison of Apriori Algorithm with Preprocessing and FP-Growth Algorithm for Finding Frequent Data Pattern in Association Rule

DOI

10.2991/aisr.k.200424.047How to use a DOI?

Keywords

data mining, Association Rules, FP-Growth, apriori, rules

Abstract

Association Rules is a data mining method to find the relation between items called rules. Finding rules in the association method can be divided into two phases. The first phase is finding the frequent pattern which satisfies specified minimum frequent, and the second phase is finding strict rules from the frequent pattern which satisfy the minimum support and confidence. The main problem of Association Rules is based on the algorithm used, and this method takes a large amount of memory and time-consuming. This study aims to add preprocessing using the aggregate function on the Apriori Algorithm and therefore improve the memory and time consumption for finding a large number of rules.

Copyright

© 2020, 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  - Deo WICAKSONO
AU  - Muhammad Ihsan JAMBAK
AU  - Danny Matthew SAPUTRA
PY  - 2020
DA  - 2020/05/06
TI  - The Comparison of Apriori Algorithm with Preprocessing and FP-Growth Algorithm for Finding Frequent Data Pattern in Association Rule
BT  - Proceedings of the Sriwijaya International Conference on Information Technology and Its Applications (SICONIAN 2019)
PB  - Atlantis Press
SP  - 315
EP  - 319
SN  - 1951-6851
UR  - https://doi.org/10.2991/aisr.k.200424.047
DO  - 10.2991/aisr.k.200424.047
ID  - WICAKSONO2020
ER  -