difference between predictive modeling and clustering?
Predictive models are sometimes called learning with a teacher, whereas in clustering you're left completely alone. 

Predictive models split data into training and testing subsample which is used for verifying computed model. Predictive (or regression) model typically assign weights to each attribute. From clustering you can compute some internal evaluation metric, but that doesn't necessarily correlate with (desired) human judgment. Unsupervised learning (clustering) mostly treats all attributes as equal as without external information, as one can't say which attribute is more important than the other.