Uncertainty in spatial data mining

Spatial data mining refers to extracting and "mining" the hidden, implicit, valid, novel and interesting spatial or non-spatial patterns or rules from large-amount, incomplete, noisy, fuzzy, random, and practical spatial databases. In which an important issue but remains underdeveloped is to reveal and handle the uncertainties in spatial data mining.. In this paper, uncertainty of spatial data is briefly analyzed firstly, including the types and origins of uncertainty, their models of measurement and propagation. Then, some uncertainty factors in operation of spatial data mining are discussed and some uncertainty handling methods are adopted, including Maximum Variance data discretization and fuzzy belief function. Finally, we think the process of spatial data mining can be regarded as a complex system, a linear serial processing system in engineering control systems. An uncertainty propagation model of spatial data mining - fuzzy logic uncertainty propagation model with Creditability factor is developed. Moreover, several key problems about uncertainty handling and propagation in spatial data mining are put forward.