Mining Nested Association Patterns

We introduce the framework of mining association patterns from nested databases. Two means to nest data items, namely, set and sequence, are considered. The term \collection " refers to a piece of data obtained by such nestings. A natural binary relation de nes the generalization hierarchy among all collections. A transaction database is a set of given collections, called transactions. The problem of mining nested association patterns is to nd all collections that are generalization of some minimum fraction of transactions, called nested association patterns. Wesketch out the working idea of an algorithm for mining nested association patterns.

Mining Nested Association Patterns

We introduce the framework of mining association patterns from nested databases. Two means to nest data items, namely, set and sequence, are considered. The term "collection" refers to a piece of data obtained by such nestings. A natural binary relation defines the generalization hierarchy among all collections. A transaction database is a set of given collections, called transactions. The problem of mining nested association patterns is to find all collections that are generalization of some minimum fraction of transactions, called nested association patterns. We sketch out the working idea of an algorithm for mining nested association patterns. 1 Introduction The problem of mining association rules is to find all itemsets that are contained in some minimum fraction of transactions. Such itemsets are called association patterns. Following the work in [AIS93], several generalizations of association rules were considered. Multiple-level association rules [HF95, SA95] deal with items t..