Discovering useful patterns from multiple instance data

Abstract

Association rule mining is one of the most common data mining techniques used to identify and describe interesting relationships between patterns from large datasets, the frequency of an association being defined as the number of transactions that it satisfies. In situations where each transaction includes an undetermined number of instances (customers shopping habits where each transaction represents a different customer having a varied number of instances), the problem cannot be described as a traditional association rule mining problem. The aim of this work is to discover robust and useful patterns from multiple instance datasets, that is, datasets where each transaction may include an undetermined number of instances. We propose a new problem formulation in the data mining framework: multiple-instance association rule mining. The problem definition, an algorithm to tackle the problem, the application fields, and the relations’ quality measures are formally described. Experimental results reveal the scalability of the problem on different data dimensionality. Finally, we apply it to two real-world applications field: (1) analysis of financial data gathered from one of the most important banks in Lithuania; (2) study of existing relations between records of unemployed gathered from the Spanish public employment service.