Knowledge Mining from Large Volume of Dataset using Fuzzy Association Rule

Abstract

As we know that fuzzy association rules are used to convert crisp set elements in to fuzzy set elements like “height=long”. On other hand Association rules on crisp set are bounded with in a limit to transfer crisp set elements in to the binary values like “height = [5.5feet or above]” and it losses some information at boundaries because of its restricted nature. Today the variations of fuzzy association rule mining is most popular. As the crisp version of Apriori, fuzzy Apriori algorithms are quit inefficient for large volume of data sets. Hence it is required to bring an efficient and powerful FA rule mining for better performance over large volume of data sets. I f we compare the fuzzy Apriori with the proposed algorithm the proposed algorithm is almost 16% faster than the earlier one if both the algorithm compared together in case of very large data sets. The proposed algorithm also has excellent processing techniques to convert the non-fuzzy dataset into fuzzy dataset