In ThingWorx Analytics the Profiles are representative groups of examples that have over or under performed against the goal historically:
- This Uses historical (or back-looking) data to establish the performance of Profiles.
- Similar to signals in that it is like a magnet on data patterns, but reveals the combinations of factors that demonstrate higher or lower performance of a Topic.
- ThingWorx Analytics uses a search algorithm that builds and finds groups that meet the required conditions for a profile
- They must be large enough in size
- They must over or under perform by a defined threshold
Whereas the Clusters are groups of objects within the Dataset the behave in in a similar way or are close to each other when compared to the rest of the object in the Dataset. This similarity in behavior or proximity in distance is measured against the goal and also against other features in the Dataset (not only the goal unlike profiles). It is basically an approach of Explanatory Data Analytics.
I hope this answers your question