Approximate clustering in association rules

Data mining holds the promise of extracting unsuspected information from very large databases. A difficulty is that ways for discouvery are often drawn from methods whose amount of work increase geometrically with data quantity. Consequentially, the use of these methods is problematic in very large data bases. Categorically based association rules are a linearly complex data mining methodology. Unfortunately, rules formed from categorical data often generate many fine grained rules. The concern is how might fine grained rules be aggregated and the role that non-categorical data might have. It appears that soft computing techniques may be useful.