We applied different clustering algorithms to the task of clus- tering multi-word terms in order to reflect a humanly built ontology. Clustering was done without the usual document co-occurrence infor- mation. Our clustering algorithm, CPCL (Classification by Preferential Clustered Link) is based on general lexico-syntactic relations which do not require prior domain knowledge or the existence of a training set. Results show that CPCL performs well in terms of cluster homogeneity and shows good adaptability for handling large and sparse matrices.