A Density-Based Clustering of Spatio-Temporal Data - New Contributions in Information Systems and Technologies - page 45

6 Conclusion

Spatio-temporal data is being generated by all means of technology nowadays. It is
important to find a method to be able to analyze and discover patterns in this data,
otherwise it is useless to store if knowledge cannot be extracted from it.
The main focus of this paper is to cluster and analyze spatio-temporal data. This
study also aims to implement different spatio-temporal functions into the algorithm
and comparing them with the ones reached to find the optimum results and best
predictions.
A spatio-temporal algorithm is presented that merges the work of the spatial
algorithm, TRACLUS with the spatio-temporal distance function GenSTLIP. Three
different experiments are carried out, each one with a different data set. The results of
each experiment are analyzed and predictions are made for the motion of each data set.
The future aim of this study is to collect data sets of more complicated cases such
as traffic jams and analyze them.

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