# An Improved Apriori Algorithm for Association Rules

arXiv: https://arxiv.org/abs/1403.3948 · HTML: https://mlsanity.com/paper/1403.3948/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Mar 2014.

## Built on by (most cited first, 5 of 5)

- [Machine Learning for Missing Value Imputation](https://mlsanity.com/paper/2410.08308/) (2024)
- [Geo-FuB: A Method for Constructing an Operator-Function Knowledge Base for Geospatial Code Generation Tasks Using Large Language Models](https://mlsanity.com/paper/2410.20975/) (2024)
- [Automatically detecting activities of daily living from in-home sensors as indicators of routine behaviour in an older population](https://mlsanity.com/paper/2307.04563/) (2023)
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## Abstract (arXiv)

There are several mining algorithms of association rules. One of the most popular algorithms is Apriori that is used to extract frequent itemsets from large database and getting the association rule for discovering the knowledge. Based on this algorithm, this paper indicates the limitation of the original Apriori algorithm of wasting time for scanning the whole database searching on the frequent itemsets, and presents an improvement on Apriori by reducing that wasted time depending on scanning only some transactions. The paper shows by experimental results with several groups of transactions, and with several values of minimum support that applied on the original Apriori and our implemented improved Apriori that our improved Apriori reduces the time consumed by 67.38% in comparison with the original Apriori, and makes the Apriori algorithm more efficient and less time consuming.
