AN ADAPTIVE TRANSACTION REDUCTION APPROACH FOR MINING FREQUENT ITEMSETS: A COMPARATIVE STUDY ON DENGUE VIRUS TYPE1

Authors

  • D.KERANA HANIREX Assistant Professor,Department of CSE, Bharath University Chennai-73.
  • DR.K.P.KALIYAMURTHIE HOD,Department of CSE, Bharath University Chennai-73.

Keywords:

Data Mining, Association Rule Mining(ARM),Apriori,FP-Growth,TDTR

Abstract

Frequent itemset mining plays an essential role in mining various patterns and in real
time applications. The dataset utilised in our experimental analysis are real world data
set for Dengue Virus Type 1 (DEN1) which is obtained from GenBank:AAB27904.1
which consists of 777 amino acids.In this paper, an adaptive TDTR(Two Dimensional
Transactions Reduction) approach which we have proposed earlier is tested against this
real Dengue virus type1 dataset and finally compared with standard Apriori algorithm and
FP-Growth algorithm. The theoretical analysis and experiments prove its efficiency and
accuracy for Dengue Virus Type1 dataset . This system reveals that Leucine(L),
Phenylalanine (F),Lysine(K),Serine(S) and Glycine(G) are the dominating amino acids in
Dengue Virus Type1 which is the same results produced from Apriori algorithm and FPGrowth
Algorithm with high performance.

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Published

30.06.2015

How to Cite

D.KERANA HANIREX, & DR.K.P.KALIYAMURTHIE. (2015). AN ADAPTIVE TRANSACTION REDUCTION APPROACH FOR MINING FREQUENT ITEMSETS: A COMPARATIVE STUDY ON DENGUE VIRUS TYPE1. International Journal of Pharma and Bio Sciences, 6(2), 336–340. Retrieved from https://ijpbs.net/index.php/journal/article/view/4234

Issue

Volume 6, Issue 2, 2015

Section

Research Articles

Categories

License

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