Data analysis tasks often require grouping of information to identify trends and associations. However, as the number of elements rises to the hundreds and thousands the cost of having a person perform the groupings unassisted quickly becomes prohibitive. Previous approaches have combined traditional clustering techniques with manual interaction steps, yielding human-in-the-loop clustering algorithms that incorporate user feedback by reweighting features or adjusting a similarity function. But in the real world, many grouping tasks lack both a feature set and a well-defined (dis)similarity metric, having only a subject matter expert with an implicit understanding of the correct relationships between elements based on the domain and the task at hand. We present a refine-and-lock clustering interaction model and demonstrate its effectiveness for cognitive-assisted human clustering over other interaction models such as split/merge and must-link/can't-link. Our approach offers effective automatic clustering assistance even in the absence of clear features or a definitive similarity metric; ensures that every cluster has final user approval; and exhibits at least a 3.94x improvement over other interactive clustering approaches in time to completion.