Abstract

One of the important problems in data mining is discovering association rules from spatial gene expression data where each transaction consists of a set of genes and probe patterns. The most time consuming operation in this association rule discovery process is the computation of the frequency of the occurrences of interesting subset of genes (called candidates) in the database of spatial gene expression data. A fast algorithm has been proposed for generating frequent item sets without generating candidate item sets along with strong association rules. The proposed algorithm uses Boolean vector with relational AND operation to discover frequent item sets. Experimental results shows that combining Boolean Vector and relational AND operation results in quickly discovering of frequent item sets and association rules as compared to general A priori algorithm

Description