Generating Frequent Patterns from Large Datasets using Improved Apriori and Support Chaining Method
نویسندگان
چکیده
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.
منابع مشابه
An Improved Frequent Pattern Algorithm for Mining Association Rules
Data mining, also known as Knowledge Discovery in Databases (KDD) is one of the most important and interesting research areas in 21 century. Frequent pattern discovery is one of the important techniques in data mining. The application includes Medicine, Telecommunications and World Wide Web. Nowadays frequent pattern discovery research focuses on finding co-occurrence relationships between item...
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