Cloud Model-Based Spatial Data Mining

In spatial data mining, we have to deal with uncertainties in data and mining process. The nature of the uncertainties can be, for example, fuzziness and randomness. This paper proposed a cloud model-based data mining method that may simultaneously deal with randomness and fuzziness. First, cloud model is presented, which is described by using three numerical characteristics. Ex, En and He. Furthermore, three visualization methods on cloud model are further proposed, which can be produced by the cloud generators. Second, cloud model-based knowledge discovery is further developed. In cloud model context, spatial data preprocessing pays more attention to data cleaning, transform between qualitative concepts and quantitative data, data reduction, and data discretization. Spatial uncertain reasoning is in the form of linguistic antecedents and linguistic consequences, both of which are implemented by X-conditional and Y-conditional cloud generators. Spatial knowledge is represented with qualitative concepts from large amounts of data, and also the cloud model. Finally, as an example, these methods are applied to mine Baota landslide monitoring database. The experimental results show that the cloud model can not only reduce the task complexity, and improve the operational efficiency, but also enhance the comprehension of the discovered knowledge.