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Analyzing the data warehouses to foresee the patterns of the transactions often needs high computational power and memory space due to the huge set of past history of the data transactions. With the fragmented data along with the current trend of distributed systems, most of the fundamental algorithms that are initially proposed to find the association among the item-sets in the data warehouses are inefficient either in throughput or the utilization of the resources. A priori algorithm is a mostly learned and implemented algorithm that mines the data warehouses to find the associations. However, A priori is generally not an optimized algorithm.
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