Comparison of sampling size estimation techniques for association rule mining

Fast and complete retrieval of individual customer needs and “to the point” product offers are crucial aspects of customer satisfaction in todays' highly competitive banking sector. Growing number of transactions and customers have excessively boosted the need for time and memory in market basket analysis. In this paper, sampling process is included into analysis aiming to increase the performance of a product offer system. The core logic of a sample, is to dig for smaller representative of the universe, that is to generate accurate association rules. A smaller sample of the universe reduces the elapsed time and the memory consumption devoted to market basket analysis. Based on this content; the sampling methods, the sampling size estimation techniques and the representativeness tests are examined. The technique, which gives complete set of association rules in a reduced amount of time, is suggested for sampling retail banking data.