Asian Journal of Computer Science and Technology (AJCST)
An Enhanced Approach to Mine Maximal Frequent Itemset using Maximal Frequent Itemset Prima Algorithm (MFIPA)Author : R. Smeeta Mary and K. Perumal
Volume 8 No.2 Special Issue:March 2019 pp 9-12
In data mining finding out the frequent itemsets is one of the very essential topics. Data mining helps in identifying the best knowledge for different decision makers. Frequent itemset generation is the precondition and most time-consuming method for association rule mining. In this paper we suggest a new algorithm for frequent itemset detection that works with datasets in distributed manner. The proposed algorithm brings in a new method to find frequent itemset not including the necessitate to create candidate itemsets. The proposed approach could be implemented using horizontal representation for transaction datasets and allocating prime value. It explores all the frequent itemset that is present in the input and according to the support the maximum frequent itemset is identified. It was applied on different transactions database and compared with well-known algorithms: FP-Growth and Parallel Apriori with different support levels. The try out showed that the proposed algorithm attain major time improvement over both algorithms.
Data Mining, Itemset, Prima Algorithm
 U.Fayyad, G. Piatetsky-Shapiro and P. Smyth, “From data mining to knowledge discovery in databases. AI Magazine”, Vol. 17, No. 3, pp. 37-54, 1999.
 S. Pramod, and O. P.Vyas, “Survey on Frequent Item set Mining Algorithms Survey on Frequent Item set Mining Algorithms Survey on Frequent Item set Mining Algorithms”, International Journal of Computer Applications, 2015.
 JayantKayastha, and N. R. Wankhade “A Survey Paper on Frequent Itemset Mining Techniques”, International Journal of Advanced Research in Computer Science and Software Engineering Research, Vol. 6, No. 2277 128X, 2016.
 RanaIshita, and Amitrathod, “Frequent Itemset Mining in Data Mining: A Survey”, International Journal of Computer Applications, Vol. 139, No. 9, pp. 0975 – 8887, 2016.
 R. Agrawal, TImielinksi and A.Swami “Mining association rules between sets of items in large database”, The ACM SIGMOD Conference, 1993.
 J. Han, HPei and YYin, “Mining Frequent Patterns without Candidate Generation”, Conf. on the Management of Data (SIGMOD’00, Dallas, TX), 2000.
 C. L. Blake, C. J.Merz, “UCI Repository of Machine Learning Databases”, In: CA, USA: Dept. of Information and Computer Science 1998.