Fusion Based Methodology for Spatial Clustering

In this paper, a novel clustering algorithm is proposed to address the clustering problem within both spatial and non-spatial domains by employing a fusion-based approach. The motivation for this work is to overcome the limitations of the existing spatial clustering methods. In most conventional spatial clustering algorithms, the similarity measurement mainly takes the geometric attributes into consideration. However, in many real applications, there is a need to fuse the information from both the spatial and the non-spatial attributes. The goal of our approach is to create and optimize clusters, such that the data objects satisfy both spatial and non-spatial similarity constraints. The proposed algorithm first captures the spatial cores having the highest structure and then employs an iterative, heuristic mechanism to determine the optimal number of spatial cores and non-spatial clusters that exist in the data. Such a fusion-based framework allows for comparing clusters in spatial and non-spatial contexts. The correctness and efficiency of the proposed clustering algorithm is demonstrated on real world data sets.