Bagging and Distributed Association Rule Mining Techniques for Distributed Data Mining on Homogeneous Datasets

Abstract

Data Mining (DM) is the process of extracting useful unknown information from data using patterns. Distributed Data Mining (DDM) evolved from DM in recent years to mine geographical distributed data. Mining can be performed either on geographical distributed data with same attribute or different attribute. This paper implements DDM based on bagging and Distributed Association Rule Mining on soy-bean, iris and contact-lens datasets having same attribute across geographical distributed sites.
Keywords: Distributed Data Mining; classifier approach; bagging; Distributed Association Rule Mining