Data mining has made broad and significant progress since its early beginnings. Today data mining is used in a vast array of areas, and numerous commercial data mining system that are available. There are many data mining systems and research prototypes to choose from. When selecting a data mining product that is appropriate for one’s task, it is important to consider various features of data mining systems from a multidimensional point of view. Researchers have been striving to build theoretical foundations for data mining. Various clustering techniques have been used for identifying and visualizing trends in multi-attribute transactional data (e.g., hierarchical clustering techniques). In this paper, in order to compute distances (similarities) between the new cluster and each of the old clusters, complete-link clustering has been used.