Kouichirou Mori

Discovery of Association Rules from Data including Missing Values

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.

Discovery of characteristic patterns from tabular structured data including missing values

This paper proposes a method dealing with missing values in the discovery of frequent patterns. Also, it proposes a method that effectively discovers the patterns from examples composed of attributes and their values. The method generates candidate patterns based on the combination of attributes and the combination of attribute values. It evaluates the patterns based on two supports. These supports are calculated based on the number of examples that do not include missing values in attributes composing target items and in all attributes. The proposed method is verified by comparing it with the existing methods dealing with missing values.