Mining Multi-Level Frequent Itemsets under Constraints

TL;DR

This paper introduces three approaches for discovering multi-level frequent itemsets under constraints, enhancing the interpretability and usefulness of association rules by involving user constraints and concept hierarchies.

Contribution

It proposes a novel modeling technique for constraints in concept hierarchies and three distinct algorithms for constrained multi-level frequent itemset mining.

Findings

Three approaches demonstrated effectiveness in constrained multi-level itemset discovery

Enhanced interpretability of rules through user constraints and hierarchies

Framework supports more refined and useful association rule extraction

Abstract

Mining association rules is a task of data mining, which extracts knowledge in the form of significant implication relation of useful items (objects) from a database. Mining multilevel association rules uses concept hierarchies, also called taxonomies and defined as relations of type 'is-a' between objects, to extract rules that items belong to different levels of abstraction. These rules are more useful, more refined and more interpretable by the user. Several algorithms have been proposed in the literature to discover the multilevel association rules. In this article, we are interested in the problem of discovering multi-level frequent itemsets under constraints, involving the user in the research process. We proposed a technique for modeling and interpretation of constraints in a context of use of concept hierarchies. Three approaches for discovering multi-level frequent itemsets under constraints were proposed and discussed: Basic approach, "Test and Generate" approach and Pruning based Approach.