Intelligent decision is the key technology of smart systems. Data mining technology has been playing an increasingly important role in decision making activities. The introduction of weight makes the weighted frequent itemsets not satisfy the downward closure property any longer. As a result, the search space of frequent itemsets cannot be narrowed according to downward closure property which leads to a poor time efficiency. In this paper, the weight judgment downward closure property for weighted frequent itemsets and the existence property of weighted frequent subsets are introduced and proved first. The Fuzzy-based WARM satisfies the downward closure property and prunes the insignificant rules by assigning the weight to the itemset. This reduces the computation time and execution time. This paper presents an Enhanced Fuzzy-based Weighted AssociationRuleMining(E-FWARM) algorithm for efficient mining of the frequent itemsets. The pre-filtering method is applied to the input dataset to remove the item having low variance. Data discretization is performed and E-FWARM is applied for mining the frequent itemsets. The experimental results show that the proposed E-FWARM algorithm yields maximum frequent items, association rules, accuracy and minimum execution time than the existing algorithms.

Online customer reviews are the best advertisement and/or communication tools for product consistency. Consumers often prefer to do some research before agreeing to buy. Customer ratings are a significant source of knowledge for influencing consumer buying choices in the present. In this paper, we discussed SigmaCR’s aim to explore machine learning techniques and skills, natural language processing and information technology to create an intelligent device that analyses consumer product feedback via the Internet and provides comprehensive product summaries and consumer feelings. It would also be a very practical method for evaluating the power of many recent research projects, including ontology-based knowledge extraction and sentimental analysis.