ANALYSIS OF IMPROVED TDTR ALGORITHM FOR MINING FREQUENT ITEMSETS USING DENGUE VIRUS TYPE 1 DATASET: A COMBINED APPROACH

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.
  • DR.A.KUMARAVEL HOD,Department of IT, Bharath University Chennai-73.

Keywords:

Data Mining, Bio Data Mining, Association Rule Mining, Apriori, FP-Growth, Genetic, Distributed, TDTR and ITDTR

Abstract

Association rule mining is the recent data mining research.We have  presented an approach for mining frequent itemsets using Dengue virus type- 1 data set. This paper proposes an Improved Two Dimensional Transaction Reduction(ITDTR)  algorithm which is a combined approach of transaction reduction and sampling in  bio data mining. This system produces the same  frequent item sets  as  produced from Apriori algorithm and FP-Growth Algorithm with the higher performance.  This system reveals that Glycine (G), Leucine(L),  Serine(S), Lysine(K) , Phenylalanine (F) are the dominating amino acids in Dengue Virus Type-1 data set with higher accuracy and efficiency.The efficiency of this  algorithm is compared with Apriori algorithm,FP_Growth algorithm,Genetic algorithm, and TDTR1,2,3,4 algorithm which we have implemented in our previous research work.

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Published

2015-06-30

How to Cite

D.KERANA HANIREX, DR.K.P.KALIYAMURTHIE, & DR.A.KUMARAVEL. (2015). ANALYSIS OF IMPROVED TDTR ALGORITHM FOR MINING FREQUENT ITEMSETS USING DENGUE VIRUS TYPE 1 DATASET: A COMBINED APPROACH. International Journal of Pharma and Bio Sciences, 6(2), 288–295. Retrieved from https://ijpbs.in/index.php/journal/article/view/4228

Issue

Volume 6, Issue 2, 2015

Section

Research Articles

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