A scalable association rule learning heuristic for large datasets
- Resource Type
- article
- Authors
- Haosong Li; Phillip C.-Y. Sheu
- Source
- Journal of Big Data, Vol 8, Iss 1, Pp 1-32 (2021)
- Subject
- Association rule learning
Frequent itemset mining
Scalability
Graph partitioning
Apriori algorithm
FP-Growth algorithm
Computer engineering. Computer hardware
TK7885-7895
Information technology
T58.5-58.64
Electronic computers. Computer science
QA75.5-76.95
- Language
- English
- ISSN
- 2196-1115
Abstract Many algorithms have proposed to solve the association rule learning problem. However, most of these algorithms suffer from the problem of scalability either because of tremendous time complexity or memory usage, especially when the dataset is large and the minimum support (minsup) is set to a lower number. This paper introduces a heuristic approach based on divide-and-conquer which may exponentially reduce both the time complexity and memory usage to obtain approximate results that are close to the accurate results. It is shown from comparative experiments that the proposed heuristic approach can achieve significant speedup over existing algorithms.