Jing Wang

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The discovery of interesting regions in spatial datasets is an important data mining task. In particular, we are interested in identifying disjoint, contiguous regions that are unusual with respect to the distribution of a given class; i.e. a region that contains an unusually low or high number of instances of a particular class. This paper centers on the(More)
The immense explosion of geographically referenced data calls for efficient discovery of spatial knowledge. One critical requirement for spatial data mining is the capability to analyze datasets at different levels of granularity. One of the special challenges for spatial data mining is that information is usually not uniformly distributed in spatial(More)
A special challenge for spatial data mining is that information is not distributed uniformly in spatial data sets. Consequently, the discovery of regional knowledge is of fundamental importance. Unfortunately, regional patterns frequently fail to be discovered due to insufficient global confidence and/or support in traditional association rule mining.(More)
Advances in database and data acquisition technologies have resulted in an immense amount of spatial data, much of which cannot be readily explored using traditional data analysis techniques. The goal of spatial data mining is to automate the extraction of interesting and useful patterns that are not explicitly represented in spatial datasets. The(More)
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