- Rare association rule mining has received a great deal
of attention in the recent past. In this research, we
use transaction clustering as a pre-processing mechanism
to generate rare association rules. The basic
concept underlying transaction clustering stems from
the concept of large items as defined by traditional
association rule mining algorithms. We make use of
an approach proposed by Koh & Pears (2008) to cluster
transactions prior to mining for association rules.
We show that pre-processing the dataset by clustering
will enable each cluster to express their own associations
without interference or contamination from
other sub groupings that have different patterns of
relationships. Our results show that the rare rules
produced by each cluster are more informative than
rules found from direct association rule mining on the
unpartitioned dataset.
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