Minimum Variance Associations - Discovering Relationships in Numerical Data

The paper presents minimum variance patterns: a new class of itemsets and rules for numerical data, which capture arbitrary continuous relationships between numerical attributes without the need for discretization. The approach is based on finding polynomials over sets of attributes whose variance, in a given dataset, is close to zero. Sets of attributes for which such functions exist are considered interesting. Further, two types of rules are introduced, which help extract understandable relationships from such itemsets. Efficient algorithms for mining minimum variance patterns are presented and verified experimentally.