Spatial data mining is a process used to discover interesting but not explicitly available, highly usable patterns embedded in both spatial and nonspatial data, which are possibly stored in a spatial database. An important application of spatial data mining methods is the extraction of knowledge from a Geographic Information System (GIS). INGENS (INductive GEographic iNformation System) is a pr...

Spatial Data Mining (SDM) is the process of mining spatial databases. Spatial databases contain details of geographical objects. Spatial data is generally associated with non-spatial data as well. Queries on spatial databases can also predicates to obtain the required results. However, the predicates are pertaining to the geographical features of spatial objects. The applications that use spati...

data sanitization is a process that is used to promote the sharing of transactional databases among organizations and businesses, it alleviates concerns for individuals and organizations regarding the disclosure of sensitive patterns. it transforms the source database into a released database so that counterparts cannot discover the sensitive patterns and so data confidentiality is preserved ag...

The importance of spatial data mining is growing with the increasing incidence and importance of large geo-spatial datasets such as maps, location based mobile app data, medical data, crime data, education system data, traffic data and many more. Co-location pattern mining is one of the important task in spatial data mining. The co-location patterns represent subsets of Boolean spatial features...

traditional leveraging statistical methods for analyzing today’s large volumes of spatial data have high computational burdens. to eliminate the deficiency, relatively modern data mining techniques have been recently applied in different spatial analysis tasks with the purpose of autonomous knowledge extraction from high-volume spatial data. fortunately, geospatial data is considered a proper s...

The goal of data mining is to discover nuggets. Spatial data mining discovers collocation rules. Especially in spatial data mining, when spatial data is relatively represented with time series, a spatio-temporal significance is inferred. In this context the collocation rule that is a quintessence for the spatial data, obtains changes to its size and shape with temporal influence. Thus, the chan...

The explosive growth of spatial data and widespread use of spatial databases emphasize the need for the automated discovery of spatial knowledge. Spatial data mining is the process of discovering interesting and previously unknown, but potentially useful patterns from spatial databases. The complexity of spatial data and intrinsic spatial relationships limits the usefulness of conventional data...

Voluminous geographic data have been, and continue to be, collected with modern data acquisition techniques such as global positioning systems (GPS), high-resolution remote sensing, location-aware services and surveys, and internet-based volunteered geographic information. There is an urgent need for effective and efficient methods to extract unknown and unexpected information from spatial data...