Home // IMMM 2011, The First International Conference on Advances in Information Mining and Management // View article
Authors:
Eloy Gonzales
Takafumi Nakanishi
Koji Zettsu
Keywords: association rule mining, heterogeneous databases, missing values, evolutionary computation.
Abstract:
Association Rule Mining is an important data mining task and it has been studied from different perspectives. Recently multi-relational rule mining algorithms have beendeveloped due to many real-world applications. However, current work has generally assumed that all the needed data to build an accurate model resides in a single database. Many practical settings, however, require the combination of tuples from multiple databases to obtain enough information to build appropriate models for extracting association rules. Such databases are often autonomous and heterogeneous in their schemes and data. In this paper, a method for association rule mining from large, heterogeneous and incomplete databases is proposed using an evolutionary method named Genetic Network Programming (GNP). Some other association rule mining methods can not handle incomplete data directly. GNP uses direct graph structure and is able to extract rules without generating frequent itemsets. The performance of the method is evaluated using real scientific heterogeneous databases with a high rate of missing data.
Pages: 113 to 120
Copyright: Copyright (c) IARIA, 2011
Publication date: October 23, 2011
Published in: conference
ISSN: 2326-9332
ISBN: 978-1-61208-162-5
Location: Barcelona, Spain
Dates: from October 23, 2011 to October 29, 2011