Various clustering algorithms have been developed to group data into clusters in diverse domains. However, these clustering algorithms work effectively either on pure numeric data or on pure categorical data, most of them perform poorly on mixed categorical and numeric data types. In this paper, a new two-step clustering method is presented to find clusters on this kind of data. In this approach the items in categorical attributes are processed to construct the similarity or relationships among them based on the ideas of co-occurrence; then all categorical attributes can be converted into numeric attributes based on these constructed relationships. Finally, since all categorical data are converted into numeric, the existing clustering algorithms can be applied to the dataset without pain. Nevertheless, the existing clustering algorithms suffer from some disadvantages or weakness, the proposed two-step method integrates hierarchical and partitioning clustering algorithm with adding attributes to cluster objects. This method defines the relationships among items, and improves the weaknesses of applying single clustering algorithm. Experimental evidences show that robust results can be achieved by applying this method to cluster mixed numeric and categorical data.
|Number of pages||9|
|Journal||Tamkang Journal of Science and Engineering|
|Publication status||Published - 2010 Mar 1|
All Science Journal Classification (ASJC) codes