In the previous post, we have summarized C4.5 decision tree induction. Well, since my thesis is about distributed streaming machine learning, it’s time to talk about streaming decision tree induction and I think it’s better start with defining “streaming machine learning” in general.
Streaming Machine Learning
Streaming machine learning can be interpreted as performing machine learning in streaming setting. In this case, streaming setting is characterized by:
- High data volume and rate, such as transactions logs in ATM and credit card operations, call log in telecommunication company, and social media data i.e. Twitter tweet stream or Facebook status update stream
- Unbounded, which means these data always arrive to our system and we won’t be able to fit them in memory or disk for further analysis with the techniques. Therefore, this characteristic implies we are limited to analyse the data once and there is little chance to revisit the data