Factors causing uncertainties in spatial data mining

Spatial data mining is to extract the unknown knowledge from a large-amount of existing spatial data repositories areas (Ester et al., 2000). The spatial data are to represent the spatial existence of an object in the infinitely complex world. They may be incomplete, noisy, fuzzy, random, and practical because the computerized entities are different from what they are in the real spatiotemporal space, i.e., observed data different from true data. For it works with the spatial database as a surrogate for the real entities in the spatial world, spatial data mining is unable to avoid the uncertainties. If the uncertainties are made appropriate use of, it may be able to avoid the mistaken knowledge discovered from the mistaken spatial data. The uncertainty parameters, such as, supportable level, confident level and interesting level, may further decrease the complexity of spatial data mining. Otherwise, it is unable to discover suitable knowledge from spatial databases via taking the place of both certainties and uncertainties with only certainties. Based on the unsuitable even mistaken knowledge, the spatial decision may be made incorrectly. The uncertainties mainly arise from the complexity of the real world, the limitation of human recognition, the weakness of computerized machine, or the shortcomings of techniques and methods. Their current constraints might further propagate even enlarge the uncertainty during the mining process.