A Survey of Fuzzy Based Association Rule Mining to Find CoOccurrence Relationships

Abstract

Data mining is the analysis step of the Knowledge Discovery in Databases process, or KDD. It is the process that results in the discovery of new patterns in large data sets. It utilizes methods at the intersection of artificial intelligence, machine learning, statistics, and database systems. The overall goal of the data mining process is to extract knowledge from an existing data set and transform it into a human-understandable structure.In data mining, association rule learning is a popular and well researched method for discovering interesting relations between variables in large databases. Association rules are usually required to satisfy a user-specified minimum support and a user-specified minimum confidence at the same time. A fuzzy association rule mining (firstly expressed as quantitative association rule mining) has been proposed using fuzzy sets such that quantitative and categorical attributes can be handled. A fuzzy rule represents each item as pair. Fuzzy logic softens the effect of sharp boundary intervals and solves the problem of uncertainty present in data relationships.In this paper we represent a survey of Association Rule Mining Using Fuzzy Algorithm. The techniques are categorized based upon different approaches. This paper provides the major advancement in the approaches for association rule mining using fuzzy algorithms.