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In this thesis, we use the knowledge about the sorts of items
and transactions to discover association rules among items in a
market transaction database. It is natural to divide items into
sorts: milk and bread belong to the sort of food while gloves
and hats pertain to the sort of clothing. We sort each
transaction according to the sorts of items contained by this
transaction. Then each sort of transactions will form a subset
of the entire database. To discover the association rules within
and between these subsets, two kinds of support-constraint
models with the corresponding algorithms are proposed. We claim
that such models not only enrich the semantics of rules compared
with the inceptive work but also emphasize the customer buying
patterns for both intra-sort and inter-sort merchandise. The
constraint needed when generating rules based on sorts of items
is also discussed. The experiments evaluate the performance of
these algorithms on synthetical databases of different inter-
sort patterns.
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