Enhancing quality of knowledge synthesized from multi-database mining

Abstract

Multi-database mining using local pattern analysis could be considered as an approximate method of mining multiple large databases. Thus, it might be required to enhance the quality of knowledge synthesized from multiple databases. Also, many decision-making applications are directly based on the available local patterns in different databases. The quality of synthesized knowledge/decision based on local patterns in different databases could be enhanced by incorporating more local patterns in the knowledge synthesizing/processing activities. Thus, the available local patterns play a crucial role in building efficient multi-database mining applications. We represent patterns in condensed form by employing a coding called ACP coding. It allows us to consider more local patterns by lowering further the user inputs, like minimum support and minimum confidence. The proposed coding enables more local patterns participate in the knowledge synthesizing/processing activities and thus, the quality of synthesized knowledge based on local patterns in different databases gets enhanced significantly at a given pattern synthesizing algorithm and computing resource.