Performance Analysis of Enhanced Fuzzy Association Rule Mining Algorithm with Levenstein Distance Using Contact Lens Dataset

Abstract

Association Rule Mining (ARM) with fuzzy logic perception smooth the progress of the easy process of mining of underlying frequent or recurrent patterns based on their own frequencies in the form of association rules from any transactional and relational datasets containing items to signify the most recent trends in the given dataset. These mined recurrent patterns or fuzzy association rules employ either for physical data analysis or also influenced to compel further mining tasks like categorization (classification) and collecting (clustering) which helps domain area experts to automate decision-making. In the concept of data mining, generally fuzzy Association Rule Mining (FARM) technique has been comprehensively adopted in transactional and relational datasets those datasets containing items who has a fewer to medium amount of attributes/dimensions. Few techniques have also adopted for high dimensional dataset also, but whether those techniques have also work for low dimensional datasets are yet to be proven out. Hence, in this paper we propose E-FAR-HD algorithm which is an enhanced version of FAR-HD algorithm that designed exclusively for large or high-dimensional datasets. We have designed this EFAR-HD algorithm that increases the accuracy of FAR-HD algorithm on the smaller datasets and remove the chances of misses when FAR-HD has tested on smaller datasets such as contact lens or patient dataset.