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. Spatial data mining is the process of discovering interesting characteristics and patterns that may implicitly exist in spatial database. A huge amount of spatial data and newly emerging concept of Spati...

Geographic data preprocessing is the most effort and time consuming step in spatial data mining. In order to facilitate geographic data preprocessing and increase the practice of spatial data mining, this paper presents Weka-GDPM, an interoperable module that supports automatic geographic data preprocessing for spatial data mining. GDPM is implemented into Weka, which is a free and open source ...

Mining spatial association rules is one of the most important branches in the field of Spatial Data Mining (SDM). Because of the complexity of spatial data, a traditional method in extracting spatial association rules is to transform spatial database into general transaction database. The Apriori algorithm is one of the most commonly used methods in mining association rules at present. But a sh...

Most spatial data in GIS are not independent, they have high autocorrelation. For example, temperatures of nearby locations are often related. Most of the spatial association rule mining algorithms derived from the attribute association rule mining algorithms which assume that spatial data is independent. In these situations, the rules or knowledge derived from spatial mining will be wrong. It ...

The size of spatial data is growing day by day. Spatial data mining is the technique by which one can able to extract interesting and potentially useful spatial information which is hidden in spatial databases. Efficient Spatial Data techniques are required to extract useful information from spatial data sets for effective decision making purpose and are used by various organizations. Presently...

Abstract. Extraction of interesting knowledge from large spatial databases is an important task in the development of spatial database systems. Spatial data mining is the branch of data mining that deals with spatial (location) data. Analyzing the huge amount (usually terabytes) of spatial data obtained from large databases such as credit card payments, telephone calls, environmental records, c...

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 CLARANS which is based on randomized search. We also develop two spatial data mining algorithms that use CLARAN...

The strength of a geographic information system (GIS) is in providing a rich data infrastructure for combining disparate data in meaningful ways, by using a spatial arrangement (e.g., proximity). As a toolbox, a GIS allows planners to perform spatial anal ysis using geo-processing functions, such as map overlay, connectivity measurements, or thematic map coloring. Although this makes the geogra...

Spatial data mining denotes the extraction of patterns from both spatial and aspatial data, possibly stored in a spatial database. An important application of spatial data mining methods is the extraction of knowledge from a Geographic Information System. INGENS (Inductive Geographic Information System) is a prototype GIS which integrates data mining tools to assist users in their task of topog...