Multi-Level Mining and Visualization of Informative Association Rules

Recently, there has been an increasing interest in applying association rule mining on data warehouses to identify trends and patterns that exist in the historical data present in large data warehouses. These warehouses have a complex underlying multidimensional structure and the application of traditional rule mining algorithms becomes hard. In this paper, we review and critically evaluate the techniques proposed for mining association rules from data warehouses. Literature review reveals the fact that majority of the prior approaches heavily rely on domain knowledge, require manual and user dependent discovery tasks, lack the ability of mining multi-level association rules, deficient in applying advanced rule interestingness measures and do not provide any visual assistance to analysts for the exploration of discovered rules. In order to overcome these limitations and to fill the identified research gaps, we propose a conceptual model for the discovery of multi-level mining and visualization of association rules from data warehouses. We have validated the model using two real-world datasets taken from UCI machine learning repository. Moreover, we evaluated the discovered rules using advanced interestingness criterion and the results obtained through experiments show that the rules generated with our approach are more informative and statistically interesting as compared to the previous approaches of multidimensional rule mining.