Titlebook: Spatial Data Mining; Theory and Applicati Deren Li,Shuliang Wang,Deyi Li Textbook 2015 Springer-Verlag Berlin Heidelberg 2015 Spatial data

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Remote Sensing Image Mining,difficulties that face SDM. This chapter covers a combination of inductive learning and Bayesian classification to classify RS images, the use of rough sets to describe and classify images and extract thematic information; image retrieval based on spatial statistics; and image segmentation, facial e

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https://doi.org/10.1007/978-3-662-48538-5Spatial data mining; Data field; Cloud model; Big data clustering; GIS data mining; Remote sensing image

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Methods and Techniques in SDM,Many methods and techniques can be used in SDM. Their performance—good or bad—will have a direct impact on the quality of the discovered knowledge. In this chapter, usable methods and techniques are presented based on their characteristics in the context of SDM (Li et al. .).

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Cloud Model,In SDM, mutually transforming between a qualitative concept and quantitative data is a bottleneck. In this chapter, the cloud model acts as a transforming model between a qualitative concept and its quantitative data (Li and Du .).

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SDM Systems,SDM, which was developed on the principles of software engineering as well as professional geo-spatial knowledge, can be used as an isolated system, as a plug-in software attachment to a spatial information system, or as an integral part of intelligent systems.

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Deren Li,Shuliang Wang,Deyi LiPresents up-to-date work on core theories and applications of spatial data mining, combining the principles of data mining and geospatial information science.Proposes data fields, cloud model, and min