Spatial data mining aims to uncover hidden patterns and relationships within spatial data, providing valuable insights for decision-makers and researchers.

Climate data, while important for environmental studies, is not typically considered spatial data in the context of Indian geography.

Spatial data mining specifically refers to the process of extracting valuable information and patterns from spatial data.

Cluster analysis is a technique used to identify groups of similar features or observations based on their spatial proximity or other attributes.

Spatial partitioning involves dividing a spatial dataset into smaller, non-overlapping regions or cells for efficient processing and analysis.

Association rule mining is a technique used to identify frequent patterns and associations between different spatial features or attributes.

Spatial generalization involves simplifying or abstracting spatial data by removing unnecessary details or combining similar features.

Spatial interpolation is a technique used to estimate the value of a variable at a specific location based on the values at neighboring locations.

Spatial filtering involves removing noise or unwanted information from spatial data to improve its quality and accuracy.

Spatial classification involves assigning spatial features to different categories based on their attributes or characteristics.

Spatial interpolation is a technique used to estimate the value of a variable at a specific location based on the values at neighboring locations.

Spatial regression is a technique used to identify relationships between spatial features or attributes and predict the value of a dependent variable based on the values of independent variables.

Spatial outlier detection involves identifying spatial features or observations that deviate significantly from the rest of the data.

Spatial feature selection involves identifying the most influential or important spatial features or attributes in a dataset for analysis and modeling.

Spatial model evaluation involves assessing the performance of a spatial data mining model or algorithm to determine its accuracy and effectiveness.