Over the last decades, the ability of producing and collecting data has increased dramatically and the data volume is growing rapidly. These data contain valuable information. These databases have become increasingly large, and thus more difficult to process with the available technologies. The field of Knowledge Discovery in Databases has arisen from the need to obtain useful information from these databases and since its beginning it has generated a large body of research. The central step in Knowledge Discovery in Databases is Data mining which means the process of extracting implicit information which was previously hidden and probably will be valuable. As a matter of fact, Data mining aims at discovering interesting and previously unknown patterns form data sets. The need for mining structured data has increased in the past few years. However, most data mining algorithms are not capable of working on data stored in relational databases directly. Most existing techniques are propositional and they extract patterns just from one table. Indeed, the presence of all the interesting data in a table is necessary. Therefore, it requires a pre processing step for transforming relational data into algorithm specified form. Unfortunately, it causes to lose some valuable information. One of the multi relational data mining methods is Inductive Logic Programming. ILP requires the data to be in the form of logic clauses and it requires extra effort in preprocessing step. The other approaches are Bayesian Networks, Neural Networks, Multi Relational Data Mining on relational databases and Multi Relational Database as a Set of Trees. In this research after reviewing the existing methods, relational database as a set of trees method was selected. By converting multi relational database into trees, it is possible to apply existing tree mining techniques to identify frequent patterns in this kind of databases. The frequent patterns that can be identified in such set of trees can be used as the basis for other multi relational data mining techniques. In this study, we proposed a new structure by combining two existing representations for multi relational databases which were key based tree representation and object based tree representation. We used two different tree mining algorithms to identify patterns from the trees representing multi relational database based on the proposed method. Moreover, by applying some changes in the structure of tree representation, we could use this structure for 0cm 0cm 0pt" Keywords: Multi relational data mining, relational database to trees, tree mining, medical data mining, frequent patterns.