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R dbscan package for density-based clustering. Use for DBSCAN, OPTICS, and HDBSCAN clustering.
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| name | dbscan |
| description | R dbscan package for density-based clustering. Use for DBSCAN, OPTICS, and HDBSCAN clustering. |
Density-based clustering algorithms.
library(dbscan)
# DBSCAN clustering
db <- dbscan(data, eps = 0.5, minPts = 5)
# Results
db$cluster # 0 = noise
db$eps
db$minPts
# Plot
plot(data, col = db$cluster + 1L)
hullplot(data, db)
# k-nearest neighbor distances
kNNdist(data, k = 5)
# Plot to find elbow
kNNdistplot(data, k = 5)
abline(h = 0.5, col = "red")
# OPTICS ordering
opt <- optics(data, eps = 10, minPts = 5)
# Reachability plot
plot(opt)
# Extract clusters
db <- extractDBSCAN(opt, eps_cl = 0.5)
db <- extractXi(opt, xi = 0.05)
# Plot
hullplot(data, db)
# Hierarchical DBSCAN
hdb <- hdbscan(data, minPts = 5)
# Results
hdb$cluster
hdb$membership_prob
hdb$outlier_scores
# Plot
plot(hdb)
plot(hdb, show_flat = TRUE)
# Compute LOF scores
lof_scores <- lof(data, minPts = 5)
# Higher scores = more outlier-like
plot(data, cex = lof_scores)
# k-nearest neighbors
nn <- kNN(data, k = 5)
# Results
nn$id # Neighbor indices
nn$dist # Distances
# Shared nearest neighbors
snn <- sNN(data, k = 5)
# Points in eps-neighborhood
frNN(data, eps = 0.5)
# Predict cluster for new points
predict(db, newdata = new_data, data = data)
# Use index for speed
db <- dbscan(data, eps = 0.5, minPts = 5,
search = "kdtree") # or "linear", "dist"