The evergrowing number of small devices with sensing capabilities produce massive amounts of diverse data. However, it becomes increasingly difficult to manage all these new data sources. Currently, there is no single way to represent, share, and understand IoT data, leading to information silos that hinder the realization of complex IoT/M2M scenarios. IoT/M2M scenarios will only achieve their full potential when the devices become intelligent: communicate, work and learn together with minimal human intervention. Pursuing these goals, we developed methods to estimate semantic similarity based on distributional profiles. Cluster algorithms were used to learn semantic categories and improve the model accuracy. In this paper, we discuss the impact of the clustering algorithm and respective heuristic to estimate input parameters for the task of learning semantic categories. Our evaluation has shown that K-means combined with silhouette methods achieved the higher result.