A method of association rule mining using Alternate Genetic Network Programming (aGNP) is proposed. GNP is one of the evolutionary optimization techniques, which uses directed graph structures as genes. aGNP is an extended GNP in terms of including two kinds of sets of node functions. The proposed system can extract important association rules whose antecedent and consequent are composed of the attributes of each family defined by users. The method measures the significance of association via chi-squared test using GNP's features. Rule extraction is done without identifying frequent itemsets used in Apriori-like methods. Therefore, the method can be applied to rule extraction from dense database, and can extract dependent pairs of the sets of attributes in the database. Extracted rules are stored in a pool all together through generations and reflected in genetic operators as acquired information. In this paper, we describe the algorithm capable of finding the important association rules and present some experimental results.
|ジャーナル||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|出版ステータス||Published - 2006 1 1|
|イベント||6th Industrial Conference on Data Mining, ICDM 2006 - Leipzig, Germany|
継続期間: 2006 7 14 → 2006 7 15
ASJC Scopus subject areas
- コンピュータ サイエンス（全般）