The emergence of large and distributed RDF data in the Linked Open Data cloud calls for approaches to extract useful knowledge using machine learning techniques such as clustering. However, the massive size and remote nature of RDF data hinder traditional approaches that gather the datasets onto a centralized location for analysis. In this work, we show how to implement two representative clustering algorithms using update queries against the SPARQL endpoint of the RDF store. We compare the time complexity and the communication complexity of our algorithms with of those that require direct centralized access to the data and hence have to retrieve the entire RDF dataset from the remote location. We conduct experiments on a real social network dataset and report our preliminary findings.