With enormous growth in the science and technology sector, there are large quantities of data that can be used for decision taking in analytics. Taking into account its spatial features for mining should improve decision precision. Extraction in spatial data in classical data is special in many ways compared with mining. Spatial use for data mining consists, but not restricted to, social media, disaster response and weather forecasting. The spatial database contains very various quantities of different types of spatial and non-spatial data. Interpretation and interpretation of large data far exceed human capability. This paper reviews the recent trends of spatial data mining to handle big data along with unique features distinguishing the mining of spatial data from classical data mining. This review also addresses major achievements and research needs in spatial data mining science.
Volume 12 | 07-Special Issue
Pages: 912-916
DOI: 10.5373/JARDCS/V12SP7/20202182