Frequent pattern generation algorithms for Association Rule Mining : Strength and challenges

Abstract

Data Mining is used in extracting valuable information in large volumes of data using exploration and analysis. With an enormous amount of data stored in databases and data warehouses requires powerful tools for analysis and discovery of frequent patterns and association rules. In data mining, Association Rule Mining (ARM) is one of the important areas of research, and requires more attention to explore rigorously because it is an prominent part of Knowledge Discovery in Databases (KDD). This paper present an empirical study on various algorithms for generating frequent patterns and association rules. To identifying, analyzing and understanding of the frequent patterns and related association rules from immense database, an strong tool is needed. It is observed that there is a strong need of an efficient algorithm who overcome the drawbacks of the existing algorithms. It is also found that the multiobjective association rules are more appropriate.