A New Approach to Rank Based Weighted Association Rule Mining Based on Fuzzy C- Means Algorithm

Abstract: The Association Rule Mining is defined as a process of Finding frequent patterns, associations, correlations, or causal structures among sets of items or objects in transactional databases, relational databases, and other information repositories. This method is commonly used in bioinformatics for the ranking of genes and genomes. There is a drawback, which makes the decision maker more confusion due to huge number of evolved rules. To avoid this, a weighted association rule mining called RANWAR (or) Rank based Weighted Association Rule Mining which uses our proposed rule interestingness measures, viz., rank-based weighted condensed support (WCS) and weighted condensed confidence (WCC) is proposed in this paper. Based on these measures we assign weight to the each item, which generates less number of frequent item sets than state-of-the-art association rule mining. This process is run on Gene Expression and Methylation datasets. The resulted genes of the top rules are biologically validated by Gene Ontologies (GOs) and KEGG pathway analyses. The top ranked rules extracted from RANWAR that hold poor ranks in traditional Apriori, are highly biologically significant to the related diseases. This paper report the top rules evolved from RANWAR that are not in Apriori.

Keywords: Weighted Condensed Support (WCS), Weighted Condensed Confidence (WCC), limma.