Developing applicable clinical machine learning models is a difficult task when the data includes spatial information, for example, radiation dose distributions across adjacent organs at risk. We describe co-design of modeling system, DASS, to support hybrid human-machine development and validation predictive estimating long-term toxicities related radiotherapy doses in head neck cancer patient...

In GIS or Geographic Information system technology, a vast volume of spatial data has been accumulated, thereby incurring the necessity of spatial data mining techniques. Displaying and visualizing such data items are important aspects. But no RDBMS software is loaded with displaying the spatial result over a MAP overlay or answer spatial queries like “all the points within” certain Neighborhoo...

spatial association rule mining is an important technique of spatial data mining. Mining spatial association rule is one of the most important branches in the field of spatial data, spatial data mining can extract the spatial patterns and characteristics, general relations of spatial and non spatial data and other data features in common that hidden in spatial database. This paper describes and...

Spatial data mining is the process of discovering interesting and previously unknown, but potentially useful patterns from large spatial datasets. Extracting interesting and useful patterns from spatial datasets is more difficult than extracting the corresponding patterns from traditional numeric and categorical data due to the complexity of spatial data types, spatial relationships, and spatia...