Relative Constraints as Features

One of most commonly used methods of data mining is clustering. Its goal is to identify unknown yet interesting and useful patters in datasets. Clustering is considered as unsupervised, however recent years have shown a tendency toward incorporation external knowledge into clustering methods making them semi-supervised methods. Generally, all known types of clustering methods such as partitioning, hierarchical, grid and density-based, have been adapted to use so-called constraints. By means constraints, the background knowledge can be easily used with clustering algorithms which usually leads to better performance and accuracy of clustering results. In spite of growing interest in constraint based clustering this domain still needs attention. For example, a promising relative constraints have not been widely investigated and seem to be very promising since they can be easily represent domain knowledge. Our work is another step in the research on relative constraints. We have used and simplified the approach presented in [1] so that we created a mechanism of using relative constraints as features.