In this work we present a generalization of partitional clustering aiming at the inclusion into the clustering process of both distance and direction of the point of observation towards the dataset. This is done by incorporating a new term in the objective function, accounting for the distance between the clusters’ prototypes and the point of observation. It is a well known fact that the chosen number of partitions has a major effect on the objective function based partitional clustering algorithms, conditioning both the level of granularity of the data grouping and the capability of the algorithm to accurately reflect the underlying structure of the data.