Abstract

Frequent itemset mining has developed into a critical data mining approach for a variety of study domains. The term "common patterns" refers to those that show often in datasets. Numerous methods for analyzing all common itemsets in the database have been presented. A novel hybrid method is proposed to provide a better result for online applications. Big Data stores a huge volume of data from various industrial applications. The stored information must be retrieved with valuable information from the optimized server. In this paper, the proposed SOFIM (Server Optimized Frequent Itemset Mining) technique finds the positive review-based frequent itemset and improves a storage server's performance. This can be achieved by analyzing the sentiment of a product review. The redundant reviews areavoided by checking duplication. The server performance is optimized by partially replicating the review data in multiple servers. Finally, the combined hybrid model SOFIM provides a better solution for finding frequent item sets.

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