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Knowledge acquisition techniques have been well researched in the data mining community. Such techniques, especially when used for unsupervised learning, often generate a large quantity of rules and patterns. While many rules generated are useful and interesting, some information is not captured by those rules, such as already known patterns, coincidental patterns and patterns with no significant value for the real world applications. Sustaining the interestingness of rules generated by data mining algorithm is an active and important area of data mining research. Different methods have been proposed and have been well examined for discovering interestingness in rules.
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