Self-organizing dual clustering considering spatial analysis and hybrid distance measures

Abstract

Dual clustering performs object clustering in both spatial and non-spatial domains that cannot be dealt with well by traditional clustering methods. However, recent dual clustering research has often omitted spatial outliers, subjectively determined the weights of hybrid distance measures, and produced diverse clustering results. In this study, we first redefined the dual clustering problem and related concepts to highlight the clustering criteria. We then presented a self-organizing dual clustering algorithm (SDC) based on the self-organizing feature map and certain spatial analysis operations, including the Voronoi diagram and polygon aggregation and amalgamation. The algorithm employs a hybrid distance measure that combines geometric distance and non-spatial similarity, while the clustering spectrum analysis helps to determine the weight of non-spatial similarity in the measure. A case study was conducted on a spatial database of urban land price samples in Wuhan, China. SDC detected spatial outliers and clustered the points into spatially connective and attributively homogenous sub-groups. In particular, SDC revealed zonal areas that describe the actual distribution of land prices but were not demonstrated by other methods. SDC reduced the subjectivity in dual clustering.