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Grid-based clustering algorithms are well-known due to their efficiency in terms of the fast processing time. On the other hand, when dealing with arbitrary shaped data sets, densitybased methods are most of the time the best options. Accordingly, a combination of grid and density-based methods, where the advantages of both approaches are achievable, sounds interesting. However, most of the algorithms in these categories require a set of parameters to be specified while usually it is not trivial to appropriately set them. Thus, we propose an InformationTheoretic Grid-based Clustering (ITGC) algorithm by regarding the clustering as a data compression problem. That is, we merge the neighbour grid cells (clusters) when it pays off in terms of the compression cost. Our extensive synthetic and real-world experiments show the advantages of ITGC compared to the wellknown clustering algorithms.

ITGC - Information-theoretic grid-based clustering.