The Comparative of Boolean Algebra Compress and Apriori Rule Techniques for New Theoretic Association Rule Mining Model
Somboon Anekritmongkol, Kulthon Kasamsan - · International Journal of Advancements in Computing Technology · 2011
The Data Mining refers to extracting or “mining” knowledge from large amounts of data. The Association Rule the one of technique to knowledge discovery. The Association Rule learning is a popular and well researched method for discovering interesting relations between variables in large databases. One of the most famous association rule learning algorithms is Apriori rule. Apriori algorithm is one of algorithms for generation of association rules. The drawback of Apriori Rule algorithm is the number of time to read data in the database equal number of each candidate is generated. Many research papers have been published trying to reduce the amount of time needed to read data from the database. In this paper, we propose a new algorithm that will work rapidly. Boolean Algebra Compress technique for Association Rule Mining (B-Compress). This algorithm adopts three major ideas. Firstly, compress data. Secondly, reduce the amount of times to scan database tremendously. Thirdly, reduce file size. The construction method of Boolean Algebra Compress technique for association rule mining has ten times higher mining efficiency in execution time than Apriori Rule.