Discovery of Association Rules from Data including Missing Values

Abstract

This paper proposes a method that deals with missing values in the discovery of association rules. The method deals with items composed of attributes and attribute values. The method calculates two kinds of support. One is characteristic support and the other is possible support. The former is based on the number of examples that do not include missing values in attributes composing target items. The latter is based on the number of examples that do not include missing values in all attributes. The method extracts all item sets whose characteristic supports are larger than or equal to the predefined threshold. The paper evaluates the proposed method by comparing it with the previous method and verifies the effect of the proposed method.