In 1962, G. Matheron introduced the term geostatistics to describe a scientific approach to evaluate problems in geology and mining, from ore reserve estimation to grade control. Geostatistics provides statistical methods used to describe spatial relationships among sample data and to apply this analysis to the prediction of spatial and temporal phenomena. They are used to explain spatial patte...

Spatial Data Mining is based on correlation of spatial objects in space. Mining frequent pattern from spatial databases systems has always remained a challenge for researchers. In the light of the first law of geography “everything is related to everything else but nearby things is more related than distant things” suggests that values taken from samples of spatial data near to each other tend ...

Spatial data mining is the discovery of interesting relationships and characteristics that may exist implicitly in spatial databases. To this end, this paper has three main contributions. First, we propose a new clustering method called CLARANS, whose aim is to identify spatial structures that may be present in the data. Experimental results indicate that, when compared with existing clustering...

Mining co-location patterns from spatial databases may disclose the types of spatial features which are likely located as neighbors’ in space. Accordingly, we present an algorithm previously for mining spatially co-located moving objects using spatial data mining techniques and Prim’s Algorithm. In the previous technique, the scanning of database to mine the spatial co-location patterns took mu...

Mining co-location patterns from spatial databases may disclose the types of spatial features which are likely located as neighbours in space. Accordingly, we presented an algorithm previously for mining spatially co-located moving objects using spatial data mining techniques and Prim's Algorithm. In the previous technique, the scanning of database to mine the spatial co-location patterns took ...

Spatial data mining is the process of discovering interesting and previously unknown, but potentially useful, patterns from large spatial datasets. Extracting interesting and useful patterns from spatial datasets is more diicult than extracting the corresponding patterns from traditional numeric and categorical data due to the complexity of spatial data types, spatial relationships, and spatial...

Spatial data mining is the process to mine data from spatial data sets like OpenStreetMap or any kind of geographic data. With the emergence of time, Geographic Information get more value from the researchers. This is used to mine useful data and features based on different locations. In related work, existing techniques are based on Dijkstras algorithm and Euclidean distance for spatial data m...

prolactin is mainly secreted by the anterior pituitary and is able to stimulate mammary gland development and lactation in mammalians. although prolactins share a common ancestral gene encoding, they show species specific characteristics and their efficiency may be different in various mammals. the importance of protein structures of all sequences of this hormone have been studied by various bi...

Spectral clustering in spatial data mining plays a very important and innovative role due to its capacity of handling of large size of data ,effective application of linear algebra to solve graphical representation and problems, and application of very low cost of clustering algorithms like k-nearest or є neighbourhood graph. Most of the research in this area is focused on efficient query proce...

Taking the design of data analyzing software for imaging brain function — SPM (Statistical Parameters Mapping) for reference, this study combined MATLAB, GIS and SDM organically, constructed an SDM system framework on MATLAB platform, integrated the major algorithms such as spatial association rule mining, spatial clustering analyzing, decision tree analyzing and so on, and applied the system i...