The targets of our project is to explore efficient algorithms for finding
frequent patterns in spatial context from data in relational databases
and / or web pages that contain spatial information such as addresses.
Following are examples of spatial data mining functions that we have developed
- Neighboring Class Sets
This function finds frequent neighboring class sets. Objects of each instance
of a neighboring class set lie in close to each other. For example, from
access logs of a location based service, it may find a frequent neighboring
class set, "timetable" service and "ticket" service
are frequently requested in close to each other, as circled in the map.
- Optimized Distance / Orientation
This function computes distance and / or orientation range from point objects
of a class. The distance and / or orientation range optimizes a user specified
criterion. For example, it may find "X meter distance from an ATM"
maximizes crime density.
- Optimized Region
This function computes pixel grid region that optimizes a user specified
criterion. For example, it can compute a region that maximizes crime density.
(You can find our previous works on Data Mining in this page.)