Fast Algorithm for Mining Multi-Level Association Rules in Large Databases

Association rules mining is an important data mining problem that has been studied extensively. In this paper, a simple but Fast algorithm for Intersecting attributes lists using hash Tables (FIT) is presented. FIT is designed for efficiently computing all the frequent itemsets in large databases. It deploys an idea similar to Eclat but has a much better computational performance than Eclat due to two reasons: 1) FIT makes fewer total number of comparisons for each intersection operation between two attributes lists, and 2) FIT significantly reduces the total number of intersection operations. Our experimental results demonstrate that the performance of FIT is much better than that of Eclat and Apriori algorithms.

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Incremental Updates of Discovered Multi-Level Association Rules

International Journal of Artificial Intelligence Tools ◽

10.1142/s0218213097000153 ◽

1997 ◽

Vol 06 (02) ◽

pp. 273-290 ◽

Cited By ~ 2

Author(s):

David W. Cheung ◽

Vincent T. Ng ◽

Benjamin W. Tam

Keyword(s):

Data Mining ◽

Association Rules ◽

Efficient Algorithm ◽

Cost Effective ◽

Incremental Algorithm ◽

Superior Performance ◽

Performance Study ◽

Mining Algorithm ◽

Large Databases ◽

Multi Level

Update of the single- and multi-level association rules discovered in large databases is inherently costly. The straight forward approach of re-running the discovery algorithm on the entire updated database to re-discover the association rules is not cost-effective. An incremental algorithm FUP have been proposed for the update of discovered single-level association rules. In this study, we have shown that the incremental technique in FUP can be generalized to other data mining systems. An efficient algorithm MLUp has been proposed for the updating of discovered multi-level association rules. Our performance study shows that MLUp has a superior performance over the representative mining algorithm such as ML-T2 in updating discovered multi-level association rules.

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IMIDB: An Algorithm for Indexed Mining of Incremental Databases

Journal of Intelligent Systems ◽

10.1515/jisys-2015-0107 ◽

2017 ◽

Vol 26 (1) ◽

pp. 69-85

Author(s):

Mohammed M. Fouad ◽

Mostafa G.M. Mostafa ◽

Abdulfattah S. Mashat ◽

Tarek F. Gharib

Keyword(s):

Data Structure ◽

Association Rules ◽

Efficient Algorithm ◽

Performance Comparison ◽

Incremental Mining ◽

Itemset Mining ◽

Large Databases ◽

Transactional Databases ◽

Dynamic Databases ◽

Database Size

AbstractAssociation rules provide important knowledge that can be extracted from transactional databases. Owing to the massive exchange of information nowadays, databases become dynamic and change rapidly and periodically: new transactions are added to the database and/or old transactions are updated or removed from the database. Incremental mining was introduced to overcome the problem of maintaining previously generated association rules in dynamic databases. In this paper, we propose an efficient algorithm (IMIDB) for incremental itemset mining in large databases. The algorithm utilizes the trie data structure for indexing dynamic database transactions. Performance comparison of the proposed algorithm to recently cited algorithms shows that a significant improvement of about two orders of magnitude is achieved by our algorithm. Also, the proposed algorithm exhibits linear scalability with respect to database size.

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A Hybrid Algorithm of Mining Closed Itemsets for Large Databases

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.145.292 ◽

2011 ◽

Vol 145 ◽

pp. 292-296

Author(s):

Lee Wen Huang

Keyword(s):

Data Mining ◽

Association Rules ◽

Execution Time ◽

Hybrid Algorithm ◽

Hybrid Approach ◽

Market Basket Analysis ◽

Market Basket ◽

Large Databases ◽

Closed Itemsets ◽

Simulation Results

Data Mining means a process of nontrivial extraction of implicit, previously and potentially useful information from data in databases. Mining closed large itemsets is a further work of mining association rules, which aims to find the set of necessary subsets of large itemsets that could be representative of all large itemsets. In this paper, we design a hybrid approach, considering the character of data, to mine the closed large itemsets efficiently. Two features of market basket analysis are considered – the number of items is large; the number of associated items for each item is small. Combining the cut-point method and the hash concept, the new algorithm can find the closed large itemsets efficiently. The simulation results show that the new algorithm outperforms the FP-CLOSE algorithm in the execution time and the space of storage.

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