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Home»Articles»An Enhanced Approach to Mine Maximal Frequent Itemset using Maximal Frequent Itemset Prima Algorithm (MFIPA)

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

Abstract

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.

Keywords

Data Mining, Itemset, Prima Algorithm

Full Text:

References

[1] 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.
[2] 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.
[3] 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.
[4] RanaIshita, and Amitrathod, “Frequent Itemset Mining in Data Mining: A Survey”, International Journal of Computer Applications, Vol. 139, No. 9, pp. 0975 – 8887, 2016.
[5] R. Agrawal, TImielinksi and A.Swami “Mining association rules between sets of items in large database”, The ACM SIGMOD Conference, 1993.
[6] J. Han, HPei and YYin, “Mining Frequent Patterns without Candidate Generation”, Conf. on the Management of Data (SIGMOD’00, Dallas, TX), 2000.
[7] C. L. Blake, C. J.Merz, “UCI Repository of Machine Learning Databases”, In: CA, USA: Dept. of Information and Computer Science 1998.

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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.

Editor-in-Chief
Dr. K. Ganesh
Global Lead, Supply Chain Management, Center of Competence and Senior Knowledge
Expert at McKinsey and Company, India
[email protected]
Editorial Advisory Board
Dr. Eng. Hamid Ali Abed AL-Asadi
Department of Computer Science, Basra University, Iraq
[email protected]
Dr. Norjihan Binti Abdul Ghani
Department of Information System, University of Malaya, Malaysia
[email protected]
Dr. Christos Bouras
Department of Computer Engineering & Informatics, University of Patras, Greece
[email protected]
Dr. Maizatul Akmar Binti Ismail
Department of Information System, University of Malaya, Malaysia
[email protected]
Dr. Harold Castro
Department of Systems Engineering and Computing, University of the Andes, Colombia
[email protected]
Dr. Busyairah Binti Syd Ali
Department of Software Engineering, University of Malaya, Malaysia
[email protected]
Dr. Sri Devi Ravana
Department of Information system, University of Malaya, Malaysia
[email protected]
Dr. Karpaga Selvi Subramanian
Department of Computer Engineering, Mekelle University, Ethiopia
[email protected]
Dr. Mazliza Binti Othman
Department of Computer System & Technology, University of Malaya, Malaysia
[email protected]
Dr. Chiam Yin Kia
Department of Software Engineering, University of Malaya, Malaysia
[email protected]
Dr. OUH Eng Lieh
Department of Information Systems, Singapore Management University, Singapore
[email protected]

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    Editorial Note

    Editorial Dr. K. Ganesh

    Editor-in-Chief
    Dr. K. Ganesh
    Global Lead, Supply Chain Management, Center of Competence and Senior Knowledge
    Expert at McKinsey and Company, India
    [email protected]
    Editorial Advisory Board
    Dr. Eng. Hamid Ali Abed AL-Asadi
    Department of Computer Science, Basra University, Iraq
    [email protected]
    Dr. Norjihan Binti Abdul Ghani
    Department of Information System, University of Malaya, Malaysia
    [email protected]
    Dr. Christos Bouras
    Department of Computer Engineering & Informatics, University of Patras, Greece
    [email protected]
    Dr. Maizatul Akmar Binti Ismail
    Department of Information System, University of Malaya, Malaysia
    [email protected]
    Dr. Harold Castro
    Department of Systems Engineering and Computing, University of the Andes, Colombia
    [email protected]
    Dr. Busyairah Binti Syd Ali
    Department of Software Engineering, University of Malaya, Malaysia
    [email protected]
    Dr. Sri Devi Ravana
    Department of Information system, University of Malaya, Malaysia
    [email protected]
    Dr. Karpaga Selvi Subramanian
    Department of Computer Engineering, Mekelle University, Ethiopia
    [email protected]
    Dr. Mazliza Binti Othman
    Department of Computer System & Technology, University of Malaya, Malaysia
    [email protected]
    Dr. Chiam Yin Kia
    Department of Software Engineering, University of Malaya, Malaysia
    [email protected]
    Dr. OUH Eng Lieh
    Department of Information Systems, Singapore Management University, Singapore
    [email protected]

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