Mining Emerging Gradual Patterns

Mining emerging patterns aims at contrasting data sets and identifying itemsets that characterise a data set by contrast to a reference data set, so as to capture and highlight their differences. This paper considers the case of emerging gradual patterns, to extract discriminant attribute co-variations. It discusses the specific features of these gradual patterns and proposes to transpose an efficient border-based algorithm, justifying its applicability to the gradual case. Illustrative results obtained from a UCI data set are described.