Generating Frequent Patterns from Large Datasets using Improved Apriori and Support Chaining Method

P. Alagesh Kannan ； E. Ramaraj

《Research Journal of Applied Sciences, Engineering and Technology》 10卷11期 (2015/08) Pp. 1281-1286

並列摘要

In this study, generating association rules with improved Apriori algorithm is proposed. Apriori is one of the most popular association rule mining algorithm that extracts frequent item sets from large databases. The traditional Apriori algorithm contains a major drawback. This algorithm wastes time in scanning the database to generate frequent item sets. The objective of any association rule mining algorithm is to generate association rules in a fast manner with great accuracy. In this study, a modification over the traditional Apriori algorithm is introduced. This improved Apriori algorithm searches frequent item sets from the large databases with less time. Experimental results shows that this improved Apriori algorithm reduces the scanning time as much as 67% and this algorithm is more efficient than the existing algorithm.

並列關鍵字

Apriori ； ARM ； association rule mining ； ELCAT ； frequent pattern ； large datasets ； support chaining