An Improved Apriori Algorithm for Association Rules (1403.3948v1)

Abstract: 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.

• The paper presents an improved Apriori algorithm that significantly reduces processing time for association rule mining.
• The improved algorithm targets transactions based on item support, avoiding full database scans to achieve an average processing time reduction of 67.38%.
• The improved algorithm enables faster data mining for high-volume data in domains like retail and healthcare, facilitating potential real-time applications.

An Improved Apriori Algorithm for Association Rules

This paper by Mohammed Al-Maolegi and Bassam Arkok presents a refinement of the well-known Apriori algorithm, which is pivotal in mining association rules. Association rule mining is a critical aspect of data mining concerned with discovering interesting patterns and relationships in large-scale databases. The traditional Apriori algorithm, despite its foundational role in the field, exhibits inefficiencies related to the exhaustive and repetitive scanning of entire datasets. This paper offers substantial improvements to mitigate these inefficiencies, reducing the time complexity associated with the derivation of frequent itemsets.

Limitations of the Traditional Apriori

The Apriori algorithm functions by iteratively scanning transactional data to identify frequent itemsets, relying heavily on set union operations and candidate itemset pruning. A primary limitation identified in the paper is the algorithm's computational cost, which scales poorly with a large number of transactions and low minimum support thresholds. Specifically, each pass for generating itemsets requires processing potentially large candidate sets, with subsequent database scans exacerbating performance issues. Such constraints necessitate a more efficient mechanism to reduce unnecessary computational overhead.

Proposed Enhancements to Apriori

The authors introduce an improved version of the Apriori algorithm, which strategically minimizes database scans by focusing only on targeted transactions. The enhanced approach centers around the generation of candidate itemsets by employing a more selective scanning process that leverages transaction IDs associated with minimum support counts.

1. Initial Transaction Scanning: The method begins with a comprehensive scan to generate the L1 table, summarizing items, their supports, and the transactions in which they appear.
2. Targeted Itemset Generation: For Ck generation, the algorithm conducts a self-join of L1 to form candidate k-itemsets but crucially only scans transactions containing the item with the lowest support count. This avoids scanning the entire dataset for each candidate itemset, thus significantly reducing processing time.
3. Iterative Reduction Approach: By iteratively applying this method across subsequent itemset lengths (C2, C3, ..., Ck), the improved algorithm continually narrows the transaction set that needs to be evaluated, ultimately reducing computational demands and improving throughput.

Empirical Evaluation and Results

The empirical results demonstrated that the implemented improved Apriori algorithm consistently outperforms the traditional approach in terms of processing time for both varying transaction volumes and minimum support thresholds. Specifically, the improved algorithm reduced processing time by an average of 67.38%, with enhancements ranging from 61.88% to 77.80% depending on the dataset size. When evaluated against different minimum support levels, the performance gains were even more prominent at lower thresholds, indicating the approach's robustness in scenarios requiring fine-grained pattern detection.

Implications and Future Directions

The implications of this research are considerable, contributing to more efficient data mining practices in domains requiring rapid, high-volume transaction data analysis, such as retail market basket analysis, telecommunication, and healthcare data systems. Additionally, by reducing time complexity, such an enhanced algorithm can facilitate real-time applications, enabling faster decision-making processes.

Future advancements could explore integrating this refined Apriori methodology within frameworks that address larger and more heterogeneous datasets, potentially incorporating parallel computing strategies or machine learning techniques to further optimize candidate generation and evaluation processes.

In conclusion, the proposed improvements mark a significant stride towards more efficient association rule mining, substantiating the ongoing need for refining core data mining algorithms to address modern data processing demands efficiently.