Evolutionary computation method to discover statistically characteristic itemsets

Abstract

We propose a method for discovering combinations of attributes (itemsets) against a background of statistical characteristics without obtaining frequent itemsets. The method uses evolutionary computations characterized by a network structure and a strategy to pool solutions over generations. The method directly discovers combinations of attributes such that a high correlation is observed between two continuous value variables from a database consisting of a large number of attributes as explanatory variables and two continuous value variables as objects of interest for their statistical properties. The proposed method, which seeks to achieve the discovery of small groups with statistical backgrounds from large data sets, extends the concept of frequent itemsets and provides a basis for generalizing the association rule representation.