Now-a-days, finding an interesting pattern from the given dataset is an emerging trend to learn more about user behaviour and patterns of interest. Prior work on this problem many pattern mining approaches use two-phase pattern mining with one exception that are however inefficient and scalable to mine high utility sequential pattern mining. The way mention above suffers scalability issue for numerous candidates and growing sequence. This paper proposes an approach to apply tight upper bound for pruning patterns. Whereas, the freshness lies in the implemented algorithm that helps to prune tight sequence utility. The applied data structure helps us to maintain sequence patterns whose values are greater than applied thresholds. Extensive experiments on real datasets show that the defined algorithm is able to mine high utility sequential pattern incrementally.

Data-mining, High Utility Patterns, Sequential Pattern Mining, Pattern Mining, Pruning, Itemset share framework

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