Density-Connected Sets and their Application for Trend Detection in Spatial Databases

1 Several clustering algorithms have been proposed for class identification in spatial databases such as earth observation databases. The effectivity of the well-known algorithms such as DBSCAN, however, is somewhat limited because they do not fully exploit the richness of the different types of data contained in a spatial database. In this paper, we introduce the concept of density-connected sets and present a significantly generalized version of DBSCAN. The major properties of this algorithm are as follows: (1) any symmetric predicate can be used to define the neighborhood of an object allowing a natural definition in the case of spatially extended objects such as polygons, and (2) the cardinality function for a set of neighboring objects may take into account the non-spatial attributes of the objects as a means of assigning application specific weights. Density-connected sets can be used as a basis to discover trends in a spatial database. We define trends in spatial databases...

• Type: Conference
• Year: 1997
• Where: KDD
• Authors: Martin Ester, Hans-Peter Kriegel, Jörg Sander, Xiaowei Xu
• Keywords: Algorithm, Data Mining, Density-connected Sets, KDD 1997, Spatial Databases