  We present QARMA, an efficient novel parallel algorithm for mining all
Quantitative Association Rules in large multidimensional datasets where items
are required to have at least a single common attribute to be specified in the
rules single consequent item. Given a minimum support level and a set of
threshold criteria of interestingness measures such as confidence, conviction
etc. our algorithm guarantees the generation of all non-dominated Quantitative
Association Rules that meet the minimum support and interestingness
requirements. Such rules can be of great importance to marketing departments
seeking to optimize targeted campaigns, or general market segmentation. They
can also be of value in medical applications, financial as well as predictive
maintenance domains. We provide computational results showing the scalability
of our algorithm, and its capability to produce all rules to be found in large
scale synthetic and real world datasets such as Movie Lens, within a few
seconds or minutes of computational time on commodity hardware.
