Increase in the range of real world applications has led to market data analysis and stock market predictions, thus an emergence of High Utility Itemset (HUI) as one of the most significant research issues. Mining HUI is a technique used to discover itemsets with utility values above a given thresholdin a transaction database. HUI reflects the impact of different items and helps in decision-making process of many applications. Algorithms that can efficiently prune candidates are known to be more efficient. "Mining top K-HUI" can be accomplished by three distinct algorithms such as, Vertical Frequent Format Mining algorithm, Maximum Utility Growth algorithm and Top K High Utility algorithm. An attempt is made to study the behavior of algorithms in terms of efficiency by measuring effectiveness in pruning candidates. To demonstrate the same, in this paper we have considered pharmacy dataset of Mysuru district for the experimentation.

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High Utility Mining Top-k Utility Item Pattern Mining