On the Relationships between Clustering and Spatial Co-location Pattern Mining

The goal of spatial co-location pattern mining is to find subsets of spatial features frequently located together in spatial proximity. Example co-location patterns include services requested frequently and located together from mobile devices (e.g., PDAs and cellular phones) and symbiotic species in ecology (e.g., Nile crocodile and Egyptian plover). Spatial clustering groups similar spatial objects together. Reusing research results in clustering, e.g. algorithms and visualization techniques, by mapping colocation mining problem into a clustering problem would be very useful. However, directly clustering spatial objects from various spatial features may not yield well-defined colocation patterns. Clustering spatial objects in each layer followed by overlaying the layers of clusters may not applicable to many application domains where the spatial objects in some layers are not clustered. In this paper, we propose a new approach to the problem of mining co-location patterns using cl...

• Type: Conference
• Year: 2006
• Where: ICTAI
• Authors: Yan Huang, Pusheng Zhang
• Keywords: Artificial Intelligence, ICTAI 2006, Spatial, Spatial Features, Spatial Objects