Mining multiple large data sources

Abstract

Effective data analysis using multiple databases requires highly accurate patterns. Local pattern analysis might extract low quality patterns from multiple large databases. Thus, it is necessary to improve mining multiple databases using local pattern analysis. We present existing specialized as well as generalized techniques for Milling multiple large databases. We formalize the idea of multi-database mining using local pattern analysis and propose a nest, generalized technique for mining multiple large databases. It improves the quality of synthesized global patterns significantly. We conduct experiments on both real and synthetic databases to judge the effectiveness of the proposed technique.