Study of Various Association Rule Mining Techniques with Positive and Negative Integration

Abstract

Data mining involves the use of sophisticated data analysis tools to discover previously unknown, valid patterns and relationships in large data sets. Association Rule Mining (AM) is one of the most popular data mining techniques. Association rule mining generates a large number of rules based on support and confidence. However, post analysis is required to obtain interesting rules as many of the generated rules are useless. In this paper, we provide some fundamental concepts related to association rule mining and survey the record of existing association rule mining methods with positive and negative integration. Obviously, a single article cannot be a entire review of the entire algorithms, yet we wish that the references cited will cover up the major theoretical issues, guiding the researcher in motivating research information that have yet to be explored.