Summary grids: building accurate multidimensional histograms

Data summarization is very important for many data analysis tasks. In this paper we propose a simple but efficient data summarization algorithm, which outputs a histogram for multidimensional data, and make a comparative study of its usage with different distributions and with existing algorithms. The idea is to iteratively grow and modify regions of homogeneous data. This is a different strategy from the commonly used strategy of iteratively fracturing subspaces using straight lines. This work compares both strategies and concludes that the new technique is better and helds good results. We also concluded that discriminate handling of outliers is important to provide good approximates.