Weighted Frequent Neighbourhood Pattern Mining
Weighted frequent neighborhood pattern mining involves the discovery of frequent patterns in a spatial dataset where patterns are assigned different weights based on their significance and proximity to other patterns. Neighborhood patterns refer to groups of spatially related objects or events that occur frequently and exhibit a regular or repeating structure. In weighted frequent neighborhood pattern mining,the significance of a pattern is determined not only by its frequency but also by the cumulative weights of its neighboring patterns.
Applications: Urban Planning, Environmental Monitoring, Transportation Planning.