The search for interesting association rules is an important topic in
knowledge discovery in spatial gene expression databases. The set of admissible
rules for the selected support and confidence thresholds can easily be
extracted by algorithms based on support and confidence, such as Apriori.
However, they may produce a large number of rules, many of them are
uninteresting. The challenge in association rule mining (ARM) essentially
becomes one of determining which rules are the most interesting. Association
rule interestingness measures are used to help select and rank association rule
patterns. Besides support and confidence, there are other interestingness
measures, which include generality reliability, peculiarity, novelty,
surprisingness, utility, and applicability. In this paper, the application of
the interesting measures entropy and variance for association pattern discovery
from spatial gene expression data has been studied. In this study the fast
mining algorithm has been used which produce candidate itemsets and it spends
less time for calculating k-supports of the itemsets with the Boolean matrix
pruned, and it scans the database only once and needs less memory space.
Experimental results show that using entropy as the measure of interest for the
spatial gene expression data has more diverse and interesting rules.