Over the last decades, due to the advances in information technologies, both the industrial and scientific communities have acquired large volumes of data in digital form. Most of these data sets are stored using relational databases consisting of multiple tables and associations. Moreover, the data used in the fields of bio-informatics, computational biology, HTML and XML documents are relational in nature. However, most of the existing approaches to knowledge discovery in databases, assume that the data are stored in a single table. Therefore, new algorithms are needed in order to exploit the relational information provided in these data sets. This thesis proposes two novel solutions to the task of supervised classification in relational domains, based on traditional non-parametric classifiers and built upon relational algebra. The first approach is based on Kernel Density Estimation, and the second technique is based on Gaussian Mixture Models. Both techniques are evaluated using three real world relational data sets, drawn from the fields of organic chemistry, medicine and genetics.