NEW FAST ALGORITHM FOR INCREMENTAL MINING OF ASSOCIATION RULES

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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Closed-Itemset Incremental-Mining Problem

Encyclopedia of Data Warehousing and Mining ◽

10.4018/978-1-59140-557-3.ch029 ◽

2011 ◽

pp. 150-153

Author(s):

Luminita Dumitriu

Keyword(s):

Association Rules ◽

Frequent Itemsets ◽

Frequent Itemset ◽

Incremental Mining ◽

Minimum Support ◽

Confidence Threshold ◽

Support Threshold ◽

Mining Association Rules

Association rules, introduced by Agrawal, Imielinski and Swami (1993), provide useful means to discover associations in data. The problem of mining association rules in a database is defined as finding all the association rules that hold with more than a user-given minimum support threshold and a user-given minimum confidence threshold. According to Agrawal, Imielinski and Swami, this problem is solved in two steps: 1. Find all frequent itemsets in the database. 2. For each frequent itemset I, generate all the association rules I’ÞI\I’, where I’ÌI.

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A Fast Algorithm of Mining Multidimensional Association Rules Frequently

2006 International Conference on Machine Learning and Cybernetics ◽

10.1109/icmlc.2006.258605 ◽

2006 ◽

Cited By ~ 4

Author(s):

Wan-xin Xu ◽

Ru-jing Wang

Keyword(s):

Association Rules ◽

Fast Algorithm

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A fast algorithm for classification based on association rules

2012 IEEE International Conference on Granular Computing ◽

10.1109/grc.2012.6468693 ◽

2012 ◽

Author(s):

Loan T.T. Nguyen ◽

Bay Vo ◽

Tzung-Pei Hong ◽

Hoang Chi Thanh

Keyword(s):

Association Rules ◽

Fast Algorithm

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An adaptive algorithm for incremental mining of association rules

Proceedings Ninth International Workshop on Database and Expert Systems Applications (Cat. No.98EX130) ◽

10.1109/dexa.1998.707409 ◽

2002 ◽

Cited By ~ 34

Author(s):

N.L. Sarda ◽

N.V. Srinivas

Keyword(s):

Association Rules ◽

Adaptive Algorithm ◽

Incremental Mining

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An Efficient Genetic Simulated Annealing Association Rules Method

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.34-35.927 ◽

2010 ◽

Vol 34-35 ◽

pp. 927-931

Author(s):

Jun Jie Cen ◽

Guo Hong Gao ◽

Ying Jun Wang

Keyword(s):

Genetic Algorithm ◽

Simulated Annealing ◽

Data Analysis ◽

Association Rules ◽

Association Rule ◽

Web Site ◽

Web Mining ◽

Incremental Mining ◽

Mining Technology

Association rule is one of the important models of Web mining. By analyzing the topology of web site, this paper brings forward an efficient genetic simulated annealing association rules method．It applies genetic algorithm，incremental mining technology to trace users access behavior and optimizes association rules，and forecast capable association rules which improves its precision．Finally, this paper gives out the data analysis of experiment and summarizes the characteristics of genetic mining．

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FIT: A Fast Algorithm for Discovering Frequent Itemsets in Large Databases

Computing Letters ◽

10.1163/1574040054861285 ◽

2005 ◽

Vol 1 (3) ◽

pp. 129-135

Author(s):

Jun Luo ◽

Sanguthevar Rajasekaran

Keyword(s):

Data Mining ◽

Association Rules ◽

Fast Algorithm ◽

Frequent Itemsets ◽

Experimental Results ◽

Important Data ◽

Computational Performance ◽

Large Databases ◽

Intersection Operation ◽

Better Than

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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