The best characterization I have heard recently distinguished between "traditional statistics" and "data mining". The key factor in the distinction was that in traditional statistics, you test hypotheses against data whereas in data mining you generate hypotheses (called models) from the data.
In my view, machine learning is pretty closely synonymous with data mining and the key distinction is learning from the data. If you exclude LDA, then you exclude k-means (which is essentially the same algorithm), but both are classic unsupervised learning applications. FPgrowth is in much the same category as clustering. I think it is a mistake to assume that only supervised learning is machine learning. On Tue, May 11, 2010 at 2:08 AM, Robin Anil <[email protected]> wrote: > Just a thought. "scalable machine learning and data-mining libraries" ?. > FPgrowth is not machine learning. Similary LDA is not machine learning but > more like data modelling. I know, its all fuzzy, and wish we had a better > way to say it. "tools for understanding patterns from data and predicting > from learned ones" >
