Spread the regular HDBSCAN cases over distinct data situations

The four datasets in the regular config were picked for size, which left two
near-duplicate pairs. Each now covers a different regime:

* kd_tree, skin_segmentation 100000 x 3 -- real, discrete, duplicate-heavy, so
  low dimension with thousands of small clusters and a tie-dense mutual
  reachability graph.
* kd_tree, make_blobs 100000 x 8 with 100 centers (was 16 features, 10 centers)
  -- many well-separated clusters, which moves the cost into the condensed tree
  and the cluster selection instead of the neighbors search. Also no longer a
  subset of the weekly sweep, which covers 10 centers at 4 / 16 / 64 features.
* brute, mnist 20000 x 784 -- high dimension, where the pairwise distance
  computation dominates.
* brute, make_blobs 25000 x 4 (was 256 features) -- the opposite brute-force
  regime: the distance computation is negligible and the passes over the n^2
  matrix dominate.

Still 8 cases. Verified end to end against stock scikit-learn: 345.8x, 176.7x,
5.2x and 5.7x respectively, with matching cluster counts and Davies-Bouldin
scores.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>

Author: Alexandr-Solovev

configs/regular/hdbscan.json

@@ -11,10 +11,10 @@
                 {
                     "source": "make_blobs",
                     "generation_kwargs": {
-                        "centers": 10,
+                        "centers": 100,
                         "n_samples": 100000,
-                        "n_features": 16,
-                        "cluster_std": 2.0
+                        "n_features": 8,
+                        "cluster_std": 1.0
                     },
                     "split_kwargs": { "ignore": true }
                 }
@@ -32,8 +32,8 @@
                     "generation_kwargs": {
                         "centers": 10,
                         "n_samples": 25000,
-                        "n_features": 256,
-                        "cluster_std": 4.0
+                        "n_features": 4,
+                        "cluster_std": 1.0
                     },
                     "split_kwargs": { "ignore": true }
                 }