PERFORMANCE OF ASSOCIATION RULES FOR DENGUE VIRUS TYPE1 AMINO ACIDS USING AN INTEGRATION OF TRANSACTION REDUCTION AND RANDOM SAMPLING (TRRS) ALGORITHM

Association rule mining is the recent research area in data mining. Frequent Itemset Mining techniques is one of the prominent techniques in pattern mining. In this system frequent itemset mining is used to find the frequent amino acids patterns in Dengue Virus Type1 data set. This system uses an integration of Transaction Reduction and Random Sampling (TRRS) approach to identify the frequent patterns in Dengue Virus Type1 amino acids sequence. Our system reveals the association between the amino acids.  Our System first identifies the number of amino acids sequences suitable for each transaction and finds the number of association rules using Transaction Reduction and Random Sampling methods by varying the sample size. Experimental results show that the performance of our proposed Transaction Reduction and Random Sampling Algorithm(TRRS) works efficiently when compared to Apriori algorithm, FP Growth algorithm, Two Dimensional Transaction Reduction(TDTR) Algorithm  Improved TDTR  algorithm, Set Oriented Mining (SETM) algorithm and Improved SETM Algorithm (ISETM)  in terms of number of association rules  generated and the time taken to generate the association rules.