DBSCAN聚类仅返回单个集群与噪声点的问题排查求助

It sounds like you're stuck with DBSCAN only returning one large cluster plus noise no matter how you tweak parameters—let's break down why this might be happening and how to fix it.

Common Reasons & Practical Fixes #

1. High-Dimensional Data Curse #

Your dataset has 17 features, and DBSCAN struggles with high-dimensional data because distance metrics lose their discriminative power (most points end up having very similar distances from each other). This makes it nearly impossible for the algorithm to pick out distinct dense regions.

Fix: Reduce dimensionality first

• Use PCA to retain most variance while cutting down features:

  library(prcomp)
  # Data is already scaled, so skip scaling in PCA
  pca_result <- prcomp(data1, scale. = FALSE)
  
  # Keep components that explain 90% of variance
  cumulative_variance <- cumsum(pca_result$sdev^2 / sum(pca_result$sdev^2))
  num_components <- which(cumulative_variance >= 0.9)[1]
  data_pca <- pca_result$x[, 1:num_components]
  
  # Re-run kNN distance plot on reduced data
  kNNdistplot(data_pca, k = num_components * 2)
  abline(h = 2, lty = 3) # Adjust based on the new clear knee point
  
  # DBSCAN on PCA-transformed data
  db_pca <- dbscan(data_pca, eps = 2, minPts = num_components * 2)
  print(db_pca)
  

• Or remove highly correlated features: Use cor(data1) to identify pairs with correlation > 0.8, then drop one from each pair to reduce redundancy.

2. Suboptimal Parameter Selection #

Your choice of k=34 (2*17) is a general rule of thumb, but it might not match this dataset's actual density distribution. If the "knee" in your kNN plot is not distinct (the line stays relatively flat), the algorithm can't tell dense clusters apart.

Fix: Refine parameter selection

• Test a range of k values (e.g., 10, 17, 25) in kNNdistplot to find a clearer knee point.
• Use the OPTICS algorithm (a DBSCAN extension) to automatically detect density-based clusters without picking eps upfront:

  opt <- optics(data1, minPts = 17) # Start with minPts equal to feature count
  # Extract clusters using a smaller eps (e.g., 2)
  opt_clusters <- extractDBSCAN(opt, eps_cl = 2)
  table(opt_clusters$cluster) # Check cluster distribution
  

• Systematically tweak parameters: Fix eps (start with 1, 2, 3) and vary minPts from 5 to 30, or vice versa, to see how cluster counts change.

3. Inherent Dataset Characteristics #

Credit card transaction data often has most users following similar spending patterns, with only a small subset of outliers (e.g., high-spend users or potential fraud cases). DBSCAN correctly flags these outliers as noise, but if you want more clusters, you need to adjust how "dense" regions are defined.

Fix: Adjust thresholds for smaller clusters

• Try smaller eps values (e.g., 2 instead of 4) to split the large cluster into smaller dense groups.
• Lower minPts (e.g., 10 instead of 34) to allow the algorithm to recognize smaller, less dense clusters.

Additional Checks #

• Verify your scaled data: Run summary(data1) to confirm all features have mean ~0 and standard deviation ~1—any extreme outliers in scaled data could skew distance calculations.
• Check for hidden outliers: Use boxplot(data1) to identify and cap or remove extreme values that might be distorting the distance metric.

内容的提问来源于stack exchange，提问作者Archana Jalaja Surendran