Reduce K-Means iteration count from 200 to 100; adjust UMAP neighbor calculation for improved clustering performance

main.js

@@ -73,7 +73,7 @@ document.getElementById("kmeans-btn").onclick = async () => {
     const n = embeddings.length, dim = embeddings[0].length;
     let centroids = Array.from({ length: k }, () => embeddings[Math.floor(Math.random() * n)].slice());
     let labels = new Array(n).fill(0);
-    for (let iter = 0; iter < 200; ++iter) {
+    for (let iter = 0; iter < 100; ++iter) {
         for (let i = 0; i < n; ++i) {
             let best = 0, bestDist = Infinity;
             for (let c = 0; c < k; ++c) {
@@ -92,7 +92,8 @@ document.getElementById("kmeans-btn").onclick = async () => {
         for (let c = 0; c < k; ++c) if (counts[c]) for (let d = 0; d < dim; ++d) centroids[c][d] /= counts[c];
     }
     // UMAP for 2D projection
-    const umap = new UMAP({ nComponents: 2, nNeighbors: Math.min(15, n - 1) });
+    const nNeighbors = Math.max(1, Math.min(lines.length - 1, 15));
+    const umap = new UMAP({ nComponents: 2, nNeighbors, minDist: 0.1 });
     const proj = umap.fit(embeddings);
     // Group lines by cluster
     const clustered = Array.from({ length: k }, (_, c) => []);