Newbe to Rapidminer: Want to apply unsupervised outlier detection

There are many different outlier detection methods available, and your description is pretty vague about the nature of the task you want to accomplish.  Also the best type of outlier detection often depends on the nature of the relationships in the data and what exactly constitutes an outlier in your view. 
To get started, I'd download the free Anomaly Detection extension and take a look at the operators they have.  One simple method you could start with would be the HBOS approach, which is non-parametric and pretty simple.  It looks to identify outliers based purely on ranges.  If that doesn't suit your needs you may want to look at some density-based measures like LOF.
Without more detailed information, it is hard to make a more concrete recommendation.  One word of warning, though, with 10MM records you might want to take a sample first and try a few approaches to see what they are like.  Otherwise you might have to wait a long time for RapidMiner to process that many records, depending on the hardware resources you have available!