International Interdisciplinary Conference on Science Technology Engineering Management Pharmacy and Humanities Held on 22nd – 23rd April 2017, in Singapore ISBN: 9780998900001 175 E FAR-HD: ENHANCED FUZZY ASSOCIATION RULE MINING USING JARO-WINKLER DISTANCE FOR HIGH DIMENSIONAL DATASETS Dhiraj Kapila Ms. Harpreet Bajaj Assistant . Professor (C.S.E) Associate . Professor ( C.S.E ) D.A.V Institute of Engg & Technology D.A.V Institute of Engg & Technology Jalandhar, Punjab, India Jalandhar, Punjab, India Abstract— Association Rule Mining (ARM) with fuzzy logic theory accelerates the easy process of mining of latent frequent or recurrent patterns constructed on their own frequencies in the form of association rules from any transactional and relational datasets containing objects and items to imply 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 systematize decision-making. In the conception of data mining, generally fuzzy Association Rule Mining (FARM) technique has been expansively adopted in transactional and relational datasets those datasets containing objects and items who have a fewer to medium amount of attributes/dimensions. Few techniques have also implemented for high dimensional dataset also, but whether those techniques have also works well 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 premeditated absolutely for large or high-dimensional datasets. We have intended 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. Index Terms—Fuzzy Association Rule Mining, Fuzzy Cluster- ing, Fuzzy Partitioning, Fuzzy Relations, Partitions, Tidlists, High Dimensions, Large Datasets, Smaller Datasets, 1. INTRODUCTION Data mining is the procedure to dig out the inherent information and knowledge from the collection of, inadequate, imperfect ,noisy, fuzzy, random and disorganized data which is hypothetically functional and people do not know in advance about this concealed and hidden information [65]. The imperative difference between the outmoded data analysis technique such as query reporting and the data mining and is that the data mining is very supportive and extremely helpful to conclude knowledge and also useful in mining information based on the proposition of no clear hypothesis [66]. The most essential use of data mining is in programmed data analysis technique to come across or to catch out previously unseen or