A Novel and Efficient Spatio-Temporal Co-location Pattern Mining Algorithm

Abstract

Colocation pattern mining approaches aim at discovering neighboring relationships of distinct spatial features in geographic and temporal space.With big spatio-temporal dataset, there is large number of patterns often discovered. Then it is of importance to discover meaningful and patterns of interest which come as an aid in applications in use for humans and commercial use. To discover interesting patterns, we present in this article a co-location pattern mining algorithmic framework by considering neighbourhood, clustering, hashing, and mining methods. Neighbourhood relationship describes the closeness between the entities. The results of neighbourhood could be varied by varying the distance threshold. These objects exhibiting neighbouring entities are grouped using clustering technique. Clustering is a classical research approach that produces grouping of the entities. A clustering technique has been presented in the paper for spatio-temporal dimension that offers the advantage of faster grouping based on the neighbourhood relationship. Finally, a hash structure is utilized for faster access and retrieval of patterns. The proposed mining algorithm along with the distance and time threshold efficiently discovers the interesting spatio-temporal patterns and validates the patterns. The experiment conducted shows that the proposed co-location algorithm method yields effective and efficient outcome.