期刊名称：ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
印刷版ISSN：2194-9042
电子版ISSN：2194-9050
出版年度：2010
卷号：XXXVIII - Part 2
页码：162-166
出版社：Copernicus Publications
摘要：In recent years many attempts have been made to index, cluster, classify and mine prediction rules from increasing massive sources of spatial time-series data. In this paper, a novel approach of mining time-series data is proposed based on cloud model, which described by numerical characteristics. Firstly, the cloud model theory is introduced into the time series data mining. Time-series data can be described by the three numerical characteristics as their features: expectation, entropy and hyper-entropy. Secondly, the features of time-series data can be generated through the backward cloud generator and regarded as time-series numerical characteristics based on cloud model. In accordance with such numerical characteristics as sample sets, the prediction rules are obtained by curve fitting. Thirdly, the model of mining time-series data is presented, mainly including the numerical characteristics and prediction rule mining. Lastly, a case study is carried out for the prediction of satellite image. The results show that the model is feasible and can be easily applied to other forecasting
关键词：Spatial Data Mining; Time-series; Cloud Model; Prediction
