Parallel Approach for Finding Co-location Pattern - A Map Reduce Framework

Spatial co-location pattern mining is a sub field of data mining which is used to discover interesting patterns which are expressed as co-location rules. The objects that are frequently located in certain region are expressed as spatial co-locations. It presents a challenge for finding co-location patterns as the traditional data is considered discrete whereas the spatial objects are embedded in a continuous space. For this a join-less approach is proposed, but as the data size increases, a large amount of computation time is devoted to find co-location rules as the approach is purely sequential. We propose a parallelized join-less approach which finds the spatial neighbor relationship in order to identify co-location instances and co-location rules. The proposed work decreases the computation time drastically as it uses a Map-Reduce framework. This paper presents precise and completeness of the new approach. Finally, an experimental evaluations using synthetic data sets show the algorithm is computationally more efficient.