Correlation Mining in Graph Databases with a New Measure

Abstract

Correlation mining is recognized as one of the most important data mining tasks for its capability to identify underlying dependencies between objects. Nowadays, data mining techniques are increasingly applied to such non-traditional domains, where existing approaches to obtain knowledge from large volume of data cannot be used, as they are not capable to model the requirement of the domains. In particular, the graph modeling based data mining techniques are advantageous in modeling various real life complex scenarios. However, existing graph based data mining techniques cannot efficiently capture actual correlations and behave like a searching algorithm based on user provided query. Eventually, for extracting some very useful knowledge from large amount of spurious patterns, correlation measures are used. Hence, we have focused on correlation mining in graph databases and this paper proposed a new graph correlation measure, gConfidence, to efficiently extract useful graph patterns along with a method CGM (Correlated Graph Mining), to find the underlying correlations among graphs in graph databases using the proposed measure. Finally, extensive performance analysis of our scheme proved two times improvement on speed and efficiency in mining correlation compared to existing algorithms.