  Over the years, data mining has attracted most of the attention from the
research community. The researchers attempt to develop faster, more scalable
algorithms to navigate over the ever increasing volumes of spatial gene
expression data in search of meaningful patterns. Association rules are a data
mining technique that tries to identify intrinsic patterns in spatial gene
expression data. It has been widely used in different applications, a lot of
algorithms introduced to discover these rules. However Priori like algorithms
has been used to find positive association rules. In contrast to positive
rules, negative rules encapsulate relationship between the occurrences of one
set of items with absence of the other set of items. In this paper, an
algorithm for mining negative association rules from spatial gene expression
data is introduced. The algorithm intends to discover the negative association
rules which are complementary to the association rules often generated by
Priori like algorithm. Our study shows that negative association rules can be
discovered efficiently from spatial gene expression data.
