Association rule generation and classification with fuzzy influence rule based on information mass value

Abstract

The association rule based classification is imperative in the disease prediction owing to its high predictability. To deal with the sensitive data, we propose an algorithm using fuzzy inference set. The association rule mining is improved further by generating an associative rules for each item of the data set. The ranking of the item in the data set is based on the information mass value estimated. The mass value represents the depth of the item in the data set and its class. Selection of the certain item set is done based on the mass value of different associated items. According to the associative items selected, the association rule mining is performed. For each association rule generated, this method calculates the impact of each object from the rules based on how fuzzy rules are generated. Fuzzy impact rules indicate symptoms and diagnostic labels. A class of disease posses disease influence measure that predicts each class of disease has changed. The proposed algorithm improves the classification efficiency and reduces the error rates.