Analysing the associations between infected genes using data mining techniques
by P. Asha; S. Srinivasan
International Journal of Data Mining and Bioinformatics (IJDMB), Vol. 15, No. 3, 2016

Abstract: The process of mining data and transforming it into useful patterns took longer duration with traditional data mining techniques. This work aims at providing faster mining and decision-making. It uses hash algorithm to promote speed and efficiency of rule mining procedures. Association rules are those that narrate the relationships prevailing between attributes present in the database. Extraction of those rules which are associated is termed as mining of Association Rules. The paper explains various rule interestingness measures which would help us in retrieving only effective, best and interesting rules. There exist so many numbers of gene in a human body which may or may not be associated with each other. This work attempts to find associations between the genes and lists out which are closely associated. Given the occurrence of a particular gene, the co-occurring genes and the genes that are no way related were analysed. The work has been implemented using RStudio of R data mining Toolkit with R language. It has been tested against various benchmark data sets and other algorithms. Based on various performance measurements, it is clear that the proposed algorithm excels to a better extent with the help of different rule interestingness measures when compared to others.

Online publication date: Mon, 20-Jun-2016

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