Optimization of Association Rules Extraction Through Exploitation of Context Dependent Constraints

In recent years, the KDD process has been advocated to be an iterative and interactive process. It is seldom the case that a user is able to answer immediately with a single query all his questions on data. On the contrary, the workflow of the typical user consists in several steps, in which he/she iteratively refines the extracted knowledge by inspecting previous results and posing new queries. Given this view of the KDD process, it becomes crucial to have KDD systems that are able to exploit past results thus minimizing computational effort. This is expecially true in environments in which the system knowledge base is the result of many discoveries on data made separately by the collaborative effort of different users. In this paper, we consider the problem of mining frequent association rules from database relations. We model a general, constraint-based, mining language for this task and study its properties w.r.t. the problem of re-using past results. In particular, we individuate two class of query constraints, namely “item dependent” and “context dependent” ones, and show that the latter are more difficult than the former ones. Then, we propose two newly developed algorithms which allow the exploitation of past results in the two cases. Finally, we show that the approach is both effective and viable by experimenting on some datasets.