Cohesion based co-location pattern mining

Because of a wide range of applications, e.g., GPS applications and location based services, spatial pattern discovery is an important task in data mining. A co-location pattern is defined as a subset of spatial items whose instances are often located together in spatial proximity. Current co-location mining algorithms are unable to quantify the spatial proximity of a co-location pattern. We propose a co-location pattern miner aiming to discover co-location patterns in a multidimensional spatial structure by measuring the cohesion of a pattern. We present two ways to build the co-location pattern miner, FromOne and FromAll, in an attempt to find a balance between accuracy and runtime. Additionally, we propose a method named Fre-ball to transform a structure into a transaction database, after which any existing itemset mining algorithm can be used to find the co-location patterns. An experimental evaluation shows that FromOne and Fre-ball are more efficient than existing methods. The usefulness of our methods is demonstrated by applying them on the publicly available geographical data of the city of Antwerp in Belgium.

Publication year:2015

Keywords:Computer. Automation

BOF-keylabel:yes

Authors from:Higher Education

Accessibility:Closed