Automatic high-dimensional association rule generation for large relational data sets

Data mining extracts knowledge from a large amount of data. It has been used in a variety of applications ranging from business and marketing to bioinformatics and genomics. Many data mining algorithms currently available, however, generate relatively simple rules that include a small number of attributes. Moreover, these algorithms need to build decision trees, which take a significant amount of time due to a large number of attributes and lack of field knowledge. Thus, in this paper, we propose a method that automatically generates high-dimensional association rules in large data sets with high accuracy and broad coverage.