31. A digital processing machine used to discover trends in a database, the device comprising: a database; a digital processing apparatus, the digital processing apparatus configured to receive data and commands from a signal-bearing medium tangibly embodying a program of machine-readable instructions executable by the digital processing apparatus and used to perform a method for discovering trends in a database, said method comprising: generating a set of selected items from a database based on predefined constraints, the predefined constraints involving one or more items of a mining input expression; determining a support value for an itemset based upon the number of times the itemset appears in the database; entering the itemset into a set of large itemsets if the support value of the itemset is greater than a minimum support value and the itemset contains at least one of the selected items; and outputting an association rule when the number of times the entered itemset appears in the database bears a predetermined relationship to a number of times an associated itemset appears in the database and thereby satisfies a minimum confidence constraint.
31. A digital processing machine used to discover trends in a database, the device comprising: a database; a digital processing apparatus, the digital processing apparatus configured to receive data and commands from a signal-bearing medium tangibly embodying a program of machine-readable instructions executable by the digital processing apparatus and used to perform a method for discovering trends in a database, said method comprising: generating a set of selected items from a database based on predefined constraints, the predefined constraints involving one or more items of a mining input expression; determining a support value for an itemset based upon the number of times the itemset appears in the database; entering the itemset into a set of large itemsets if the support value of the itemset is greater than a minimum support value and the itemset contains at least one of the selected items; and outputting an association rule when the number of times the entered itemset appears in the database bears a predetermined relationship to a number of times an associated itemset appears in the database and thereby satisfies a minimum confidence constraint. 33. The machine recited in claim 31, where the method for discovering trends in a database further comprises: determining whether sets contained in the large itemsets satisfy the predefined constraints; generating a set of candidate itemsets including large itemsets which satisfy the predefined constraints; and using the set of candidate itemsets to output the association rules.