SQL Model in Language Encapsulation and Compression Technique for Association Rules Mining

Abstract

Discovery of association rules is an important for Data mining. 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 generate. 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 and without frequency tree or temporary candidate itemsets in RAM or Hard disk. SQL Model in Language Encapsulation and Compression Technique for Association Rules Mining (SMILE-ARM). This algorithm will generate candidates are greater than minimum support on-the-fly by SQL. This algorithm is based on two major ideas. Firstly, compress data. Secondly, generation of candidate itemsets on-the-fly by SQL. Based on the experimental results, an increase in the number of transactions or the number of items did not affect the speed at which candidates were generated by this algorithm. The construction method of SQL Model in Language Encapsulation and Compression Technique for Association Rules Mining (SMILE-ARM) technique has twenty times higher mining efficiency in execution time than Apriori Rule.