ABSTRACT
Histograms are one of the most used tools in exploratory data analysis. They present a graphical representation of data, providing useful information about the distribution of a random variable. Histograms are widely used for density estimation. They have been used in approximate query answering, in processing massive datasets, to provide a quick but faster answer with error guarantees. In this chapter we present representative algorithms to learn histograms from data streams and its application in data mining.
