<p>I observe that concurrent to my writing of this answer, the question has been re-opened. The question, to my reading, remains unclear, and I think the unclear elements of the question I name here still need to be addressed.</p>

<hr>

<p>Perhaps I'm overstepping, but it would seem that the real issue here that OP would like to know why, precisely, the question was closed (the closure being the genesis of the personal attacks). I submit that I have experience working on a diversity of data science/statistics tasks, but I am having some difficulty discerning what your post is about. (Perhaps this should be a comment on OP's other post, but now it's too long to fit.)</p>

<p>These are the particular areas that I think would benefit from clarification.</p>

<ul>
<li><p>You make reference to $a$, $b$, and $c$ without introducing what they
are. Is that piece of information contained in the plots? Perhaps,
but the plots are so small that I can't read them. </p></li>
<li><p>You discuss increasing/decreasing stimulus. Is a stimulus one of the
"variables"? Or is it something else? Is it synonymous with one of
your 12 observations? </p></li>
<li><p>You discuss categorization/clustering of your data. Categorization
suggests that you are performing a supervised learning task, e.g.
logistic regression, while clustering suggests an unsupervised task.
Which is it? If you know where increases or decreases occur with
respect to stimulus, you can label and classify those examples. But
it seems you hew towards the "unsupervised" meaning by reference to
DBSCAN and $k$-means. Remember that the essential part of clustering
(at least as I understand it) is that you're constructing groups of
"nearby" things, where "nearby" is characterized by the choice of a
distance metric. So perhaps deliberate selection of a distance metric
particular to your task will give traction.</p></li>
</ul>

<p>My recommendation would be to sit down with a friend (who doesn't do work in your field and who is unfamiliar with this problem) and have him or her read your question. If they're not quite sure what you're after, work with your friend on how to refine and clarify the question.</p>

<p>In general, users of CV are often working with confidential or proprietary data, which causes them to be circumspect. I appreciate the difficulty, as I once worked on data that was very sensitive. However, without some concrete notion for readers to latch on to and fix in their minds, it is very difficult to formulate an answer. If this is the case, I suggest attaching your problem to some analogous or toy scenario.</p>
