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Association rule mining is one of the most important and well-researched techniques of data mining, that aims to induce associations among sets of items in transaction databases or other data repositories. Currently Apriori algorithms play a major role in identifying frequent item set and deriving rule sets out of it. But it uses the conjunctive nature of association rules, and the single minimum...
Generally frequent itemsets are extracted from large databases by applying association rule mining (ARM) algorithms like Apriori, Partition, Pincer-Search, Incremental, and Border algorithm etc. Genetic Algorithm (GA) can also be applied to discover the frequent patterns from databases. The main advantage of using GA in the discovery of frequent patterns or itemsets is that they can perform global...
As the databases grow in size, the interpretation of data within them, is increasingly difficult to obtain, and often only stored as backup of various transactions of records. In this data there is new information which is present, but requires a higher level of processing. This will create the association rules which are used to detect events that occur together in a set of data. To find these rules,...
Genetic algorithm is an important algorithm of association rule mining. However, there is some issues that genetic algorithm easy to lead prematuring convergence and into the plight of local optimum, or convergence too much time and consume a large amount of time to search. For resolving this issues, the paper improves the algorithm through adopting an adaptive mutation rate and improving the methods...
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