Conceptual clustering is a clustering method for for producing conceptual description for each resulting group by forming a category structure hierarchy. COBWEB and ITERATE are two algorithms applying this conceptual clustering technique. COBWEB focuses on the effort to maximize the needed information through a continuous prediction process of information existing in ain a cluster. ITERATE, on the other hand, focuses on the creation of stable partition and maximizes the difference between partitions. This paper elaborates on implementation of COBWEB and ITERATE algorithms as well as shows the comparison results between the two based on partition quality and execution time. Keywords: Conceptual Clustering, Concept, Hierarchy, COBWEB, ITERATE