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The paradigm shift from 'Data-centered pattern mining' to 'Domain driven actionable knowledge discovery' has increased the need for considering the business yield (utility) and demand or rate of recurrence of the items (frequency) while mining a retail business transaction database. Such a data mining process will help in mining different types of itemsets of varying business utility and demand. The authors here present a set of algorithms for mining all types of utility and frequency based itemsets from a retail business transaction database which would significantly aid in inventory control and sales promotion.
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