PROJECT TITLE :
Efficient High Utility Pattern Mining for Establishing Manufacturing Plans with Sliding Window Control - 2017
In industrial areas, understanding the preference of customers is one of the vital issues for establishing profitable product producing plans. As one in all the approaches in pattern mining, high utility pattern mining has been employed to seek out a collection of product creating high profits by considering the purchase quantity and value of each product. In this regard, high utility pattern mining will be useful to establish profitable product manufacturing plans that enable an organization to maximise its revenue. For establishing manufacturing plans, we additionally want to perceive the recent preference of consumers from stream information, that are frequently generated without limitations. In this paper, we have a tendency to propose a completely unique algorithm and list structure for locating high utility patterns over information streams on the idea of a sliding window mode. Unlike existing algorithms, the proposed algorithm does not consume huge computational resources for verifying candidate patterns as a result of it can avoid the generation of candidate patterns. So, the algorithm efficiently works in complex dynamic systems. Experimental results obtained from various tests using real-world dataset show that the proposed algorithm outperforms state-of-the-art strategies in terms of runtime, memory usage, and scalability.
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