  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 and
class (if present). Complex models learnt computationally from the data are
more interpretable to a human analyst when such interdependencies are known. In
this paper, we focus on mining two types of association information among the
attributes - correlation information and interaction information for both
supervised (class attribute present) and unsupervised analysis (class attribute
absent). 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.
