Parallel Approach For Finding Co-Location Patterns

The book discuss about the spatial co-location mining which finds the co-locations parallel which reduces the time complexity. Spatial co-location patterns represent a subset of features whose instances are frequently co-located in close proximity; For example Mountain area and new truck purchased are frequently co-located patterns, indicating that a person living close to mountainous areas is likely to buy a truck. Since the instances of spatial features are embedded in a continuous space and share a variety of spatial relationships the implementation of co-location mining can be taken as a challenge.