Effective and Innovative Approaches for Comparing Different Multilevel Association Rule Mining for Feature Extraction: A Review
نویسندگان
چکیده
Today multilevel association rule mining is an emerging field in data mining. Its main goal is to find hidden information in or between levels of abstraction. It is mainly used for decision making for large data. It focuses on the customer relationship management. Apriori algorithm is mainly used for the multilevel association rule mining. Producing large number of candidate item sets and multiple scanning databases is main shortage of the Apriori algorithm. Because of the multilevel association execution time is reduced and throughput increase in new methods. MRA algorithm using Bayesian probability, concept hierarchy ,COFI-tree method, dynamic concept hierarchy are used for increase performance of multilevel association rule mining.
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