Integrating data mining models from distributed data sources

Abstract

Data mining has been widely applied to analyze data for decision makers. However, traditional data mining techniques are insufficient for analysis of multiple data sources. To mine multiple data sources, one possible way is reusing local data mining models discovered from each data source and searching for valid patterns that are useful at the global level. This paper presents a Knowledge Integration Model for integrating data mining models discovered from different data sources. This proposal is especially helpful for organizations which distributed data sources have been mined locally, and don't share their original databases. © 2010 Springer-Verlag Berlin Heidelberg.