Objectives: Utility mining gains more attraction in recent years. Utility mining can be defined as mining item’s utility and revealing high utility items. In this work an algorithm EHUFIM (i.e Enhanced High Utility Frequent Itemset Mining), was proposed to reveal high utility – frequent itemsets even with negative profits. Methods/Statistical Analysis: The proposed algorithm uses utility mining methods to retrieve high profitable items. Then it uses support measure to reveal high occurrence items. The algorithm implements filter procedure of HUINIV algorithm to handle negative profit items. Findings: This algorithm discovers itemset that have more frequency and high utility with negative profit. Discovering such items helps in decision making in super markets, cross product marketing etc. The proposed algorithm was executed and performance of the algorithms was calculated. Application/Improvement: Existing utility frequents mining algorithms does not consider negative profit values. But, this proposed algorithm, takes negative utility values into account.
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