Classification and clustering of granular data

Information granules are formed to reduce the complexity of the description of real-world systems. The improved generality of information granules is attained through sacrificing some of the numerical precision of point-data. In this study we consider a hyperbox-based clustering and classification of granular data, and discuss detailed criteria for the assessment of the quality of the combined classification and clustering. The robustness of the criteria is assessed on both synthetic data and real-life data from the domain of traffic control.