Data Mining vs. Machine Learning?

Hi guys,

I'm starting a PhD in Data Mining, and have mostly been equating it with Machine Learning so far until I found this quote by Kevin Murphy:

Such models often have better predictive accuracy than association rules, although they may be less interpretible. This is typical of the difference between data mining and machine learning: in data mining, there is more emphasis on interpretible models, whereas in machine learning, there is more emphasis on accurate models.

But do you guys see this difference in practice (particularly in academia)? Do people use measures of interestingness rather than straight prediction accuracy? Is time and space complexity less of a concern? Maybe data mining research focuses less on "Big Data" and uses more "medium data"? Do people really "data mine" images or text data, or is it mostly just standard databases?

Does DM have much of a presence in ML conferences? I know about ICDM, but what about others?

Basically I'm just after any general impressions people might have about the academic difference between DM and ML :)

Grasping the big picture of my research area seems pretty elusive...

Sorry about the ramble,

Thanks guys!