An efficient and robust combined clustering technique for mining in large spatial databases

Mining knowledge from large amounts of spatial data is known as spatial data mining. It becomes a highly demanding field because huge amounts of spatial data have been collected in various applications ranging from geo-spatial data to bio-medical knowledge. The amount of spatial data being collected is increasing exponentially. So, it far exceeded human's ability to analyze. Recently, clustering has been recognized as a primary data mining method for knowledge discovery in spatial database. The database can be clustered in many ways depending on the clustering algorithm employed, parameter settings used, and other factors. Multiple clusterings can be combined so that the final partitioning of data provides better clustering. Applying cluster combinations by using neural networks can yield dramatic improvements in generalization performance. Another problem with most clustering algorithms is that the user must input the desired number of clusters. Quite often the optimal number of clusters is not known prior to execution. The main objective of this paper is to propose an efficient robust combined clustering technique using neural networks for large image databases that does not require a priori knowledge of the proper number of clusters. It only requires the user to provide a maximum number of clusters. Results on real databases are given to show that the proposed robust combined clustering technique can (i) improve quality and robustness, and (ii) enable distributed clustering.