Abstract

In industrial areas, understanding the preference of customers is one of the important considerations for establishing profitable product manufacturing plans. High utility pattern mining can find a set of products creating high profits by considering the purchase quantity and price of each product. So high utility pattern mining can be useful to establish profitable product manufacturing plans that allow a corporation to maximize its revenue. For establishing manufacturing plans, we also need to understand the recent preference of customers from stream data, which are continually generated without limitations. High Utility Pattern Mining using sliding window control (SHUPM) helps to mine high utility patterns by considering quantity, price and recent purchase preference of customers. The rapid growth of data generated and stored has led us to the new era of Big Data. Existing methods in high utility mining cannot efficiently mine high utility patterns from Big Data. In this work, Hadoop’s MapReduce architecture is incorporated with SHUPM to efficiently mine itemset from Big Data. MapReduce architecture divides whole mining tasks into smaller independent subtasks and uses Hadoop distributed file system to manage distributed data so that it allows to discover HUIs from Big data in a parallel, reliable, and fault tolerance manner.

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