Spatial Clustering Algorithms and Quality Assessment
Jingke Xi · 2009
Spatial data mining (SDM) is the process of discovering interesting and previously unknown, but potentially useful patterns from large spatial databases. Being an important role of SDM, spatial clustering is to organize a set of spatial objects into groups (or clusters) such that objects in the same group are similar to each other and different from those in other groups. Spatial clustering has been extensively studied in the past decades. However, most existing research focuses on the algorithm based on special background or application, compared with spatial clustering algorithms and quality assessment is still rare. This paper firstly analyses complexity of spatial objects. Secondly, discusses and compares approach of different spatial clustering, which can be categorized into partitioning approaches, hierarchical approaches, density-based approaches, grid-based approaches and others. Thirdly, studies quality assessment for spatial clustering.