Spatial data mining is the discovery of interesting relationships and characteristics that may exist implicitly in spatial databases. In this paper, we explore whether clustering methods have a role to play in spatial data mining. To this end, we develop a new clustering method called CLAHANS which is based on randomized search. We also develop two spatial data mining algorithms that use CLAHAN...

“Spatial data mining, or knowledge discovery in spatial database, refers to the extraction of implicit knowledge, spatial relations, or other patterns not explicitly stored in spatial databases.” (Koperski and Han, 1995) Data mining, or knowledge discovery in databases, refers to the “ discovery of interesting, implicit, and previously unknown knowledge from large databases.” (Frawley et al, 1992)

Spatial data mining is to extract the unknown knowledge from a large-amount of existing spatial data repositories areas (Ester et al., 2000). The spatial data are to represent the spatial existence of an object in the infinitely complex world. They may be incomplete, noisy, fuzzy, random, and practical because the computerized entities are different from what they are in the real spatiotemporal...

In this paper we propose a method for the discovery of spatial association rules, that is, association rules involving spatial relations among (spatial) objects. The method is based on a multi-relational data mining approach and takes advantage of the representation and reasoning techniques developed in the field of inductive logic programming (ILP). In particular, the expressive power of predi...

A technique to identify storms and capture scalar features within the geographic and temporal extent of the identified storms is described. The identification technique relies on clustering grid points in an observation field to find self-similar and spatially coherent clusters that meet the traditional understanding of what storms are. From these storms, geometric, spatial, and temporal featur...

Spatial data mining requires the analysis of the interactions in space. These interactions can be materialized using distance tables, reducing spatial data mining to multi-table analysis. However, conventional data mining algorithms consider only one input table where each row is an observation to analyze. Simple relational joins between these tables does not resolve the problem and mislead the...

ion of GeoDatabases Geographic Database Conceptual Modeling Modeling with a UML Profile Geographic Databases Spatio-temporal Database Modeling with an Extended Entity-Relationship Model Geographic Dynamics, Visualization and Modeling MAY YUAN Department of Geography and Center for Spatial Analysis, University of Oklahoma, Norman, OK, USA

As stated in literature by several authors, there has been literally big-bang explosion in data acquired in recent times. This is especially so about the geographical or geospatial data. The huge volume of data acquired in different formats, structured, unstructured ways, having large complexity and non-stop generation of these data have posed an insurmountable challenge in scientific and busin...