An Efficient Pattern Mining Analysis in Health Care Database
نویسندگان
چکیده
Association Rules are discovered by identifying relationships among sets of items in a transaction database with two measures which quantify the support and confidence of the rule. Finding frequent itemsets is computationally the most expensive step in Association Rule discovery and therefore, it has attracted significant research attention. This paper reviews Apriori related and Eclat algorithms with detailed discussion about various data structures. Computation are made for our own surveyed data sets and compared. The analysis ends with various research issues like types of rules, execution time and space complexity of algorithms.
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