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Vol. 153, No. 6, June 2013, pp. 84-91

 

Bullet

 

Research of Improved Apriori Algorithm Based on Itemset Array
 
* Naili Liu, Lei Ma

Linyi University, Linyi City, 276005, China

* Tel.: 13754706905, fax: 0539-8766320

* E-mail: lnl1999@163.com

 

Received: 19 April 2013   /Accepted: 14 June 2013   /Published: 25 June 2013

Digital Sensors and Sensor Sysstems

 

Abstract: Mining frequent item sets is a major key process in data mining research. Apriori and many improved algorithms are lowly efficient because they need scan database many times and storage transaction ID in memory, so time and space overhead is very high. Especially, they are lower efficient when they process large scale database. The main task of the improved algorithm is to reduce time and space overhead for mining frequent item sets. Because, it scans database only once to generate binary item set array, it adopts binary instead of transaction ID when it storages transaction flag, it adopts logic AND operation to judge whether an item set is frequent item set. Moreover, the improved algorithm is more suitable for large scale database. Experimental results show that the improved algorithm has better efficiency than classic Apriori algorithm.

 

Keywords: Data mining, Association Rules, Frequent item sets, Binary item set array, Logic AND operation

 

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