  The multi relational data mining approach has developed as an alternative way
for handling the structured data such that RDBMS. This will provides the mining
in multiple tables directly. In MRDM the patterns are available in multiple
tables (relations) from a relational database. As the data are available over
the many tables which will affect the many problems in the practice of the data
mining. To deal with this problem, one either constructs a single table by
Propositionalisation, or uses a Multi-Relational Data Mining algorithm. MRDM
approaches have been successfully applied in the area of bioinformatics. Three
popular pattern finding techniques classification, clustering and association
are frequently used in MRDM. Multi relational approach has developed as an
alternative for analyzing the structured data such as relational database. MRDM
allowing applying directly in the data mining in multiple tables. To avoid the
expensive joining operations and semantic losses we used the MRDM technique.
This paper focuses some of the application areas of MRDM and feature directions
as well as the comparison of ILP, GM, SSDM and MRDM
