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Interestingness discovery is a process employed in data mining and knowledge discovery to classify the usefulness of patterns.
Many different patterns such as customer spending and social trends are often discovered in data mining, but the relevance, utility, or usefulness of said patterns depends upon their interestingness.
Interestingness discovery or interestingness measure is the technique used in order to narrow down the number of patterns to consider, since most of them have already been found or considered, too obvious, or even irrelevant.
A common problem in the field of knowledge discovery is the proper classification and determination of usefulness and utility of discovered patterns.
Interestingness discovery aims to provide this through the use of set algorithms in order to measure the utility and usefulness of a given pattern through its interestingness. Interestingness is discovered through interestingness measurement, which is divided into two categories: objective measurements which are based on the properties of the discovered patterns, basically statistical strength; and subjective measurement, which is based on the analyst’s view and beliefs regarding the particular domain from which the pattern has been discovered.
The interestingness of the pattern will determine whether it is new, useful. or simply interesting, or if it is old, too obvious, or irrelevant.