QARMA Quantitative Association Rules for RUL ia a new highly parallel algorithm suitable for running in elastic compute environments, for mining all interesting quantitative association rules, that is, non–dominated rules having support above a minimum support threshold Smin, and for a set of interestingness metrics (e.g. confidence) have levels above minimum specified threshold values Cmin. The scale-out capabilities of the algorithm make it particularly attractive because of the high compute intensive load it may require on large datasets.

TrendArtificial Intelligence
TypesAlgorithmData Analytics