Spatial data mining features between general data mining

Abstract

Data mining is often described as the process of finding, analyzing, and sifting through vast quantities of data in order to discover connections, patterns, or statistical correlations. The practice of finding interesting, valuable, non-trivial patterns of information or knowledge from big geographical datasets is known as spatial data mining (SDM). Due to the complexity of geographical data types, spatial connections, and spatial auto-correlation, finding interesting and meaningful patterns from spatial datasets must be more challenging than extracting the equivalent patterns from conventional numeric or categorical data. Emphasis was placed on the distinctive characteristics that differentiate geographic data mining from traditional data mining, as well as the significant achievements of spatial data mining research. In precision agriculture, community planning, resource finding, and other fields, extracting intriguing patterns and rules from spatial information, such as remotely sensed images and related ground data, may be useful.