On mining statistically significant attribute association information

Knowledge of the association information between the attributes in a data set provides insight into the underlying structure of the data and explains the relationships (independence, synergy, redundancy) between the attributes. Complex models learnt computationally from the data are more interpretable to a human analyst when such interde-pendencies are known. In this paper, we focus on mining two types of association information among the attributes - correlation information and interaction information which capture multivariate dependencies between the data attributes. Identifying the statistically significant attribute associations is a computationally challenging task - the number of possible associations increases exponentially and many associations contain redundant information when a number of correlated attributes are present. In this paper, we explore efficient data mining methods to discover non-redundant attribute sets that contain significant association information indicating the presence of informative patterns in the data.