In this talk, I will present recent advances in data stream mining,
discussing their implementations in MOA. MOA is a software environment
for running experiments for online learning from evolving data
streams. It is designed to deal with the challenging problems of
scaling up the implementation of state of the art algorithms to real
world dataset sizes and of making algorithms comparable in benchmark
streaming settings. It contains a collection of algorithms for
classification, clustering and pattern mining as well as tools for
evaluation. MOA is based on the experience of WEKA, the Waikato
Environment for Knowledge Analysis.