An integrated framework for relational and hierarchical mining of frequent closed patterns

Abstract

This paper addresses an Integrated Framework for relational and hierarchical mining of Frequent Closed Pattern. Large data banks have created the necessity to formulate a system for effective retrieval of data patterns. The major issues that have to be dealt here are granularity of patterns, effectiveness of patterns and time taken for retrieval. Here we discuss Inter-related generalized self-organizing map (IGSOM) and relational attribute-oriented induction (RAOI), which are focused on pattern extraction along with CC-MINER, a hierarchical mining technique for exploring Frequent Closed Pattern from very dense data sets. We further provide implementation results for education data set and prostrate cancer data set. © 2009 Springer Berlin Heidelberg.