CFSFDP Density Peaks Clustering Algorithm Source Code

The Density Peaks Clustering Algorithm (CFSFDP) is a clustering method based on local density and relative distance of data points. Its core concept involves detecting high-density regions in datasets and identifying cluster centers through decision graphs. The algorithm operates without predefining the number of clusters, making it suitable for discovering natural cluster structures.

### Algorithm Principles Local Density Calculation: For each data point, compute the density within its neighborhood. Common implementations use either cutoff distance (counting points within a radius) or Gaussian kernel functions to define density. Code implementation typically involves distance matrix computation and neighborhood aggregation. Relative Distance Measurement: For each point, identify the nearest point with higher density as its "nearest higher-density neighbor". This requires sorting densities and calculating minimum distances among higher-density points. Decision Graph Construction: Plot density versus relative distance to create a decision graph. Cluster centers are selected as points exhibiting both high density and large relative distances from other high-density points. Cluster Assignment: Non-center points are assigned to the same cluster as their nearest higher-density neighbor, implemented through recursive or iterative propagation algorithms.

### Source Code Implementation Key Points Density Calculation Optimization: Use spatial indexing structures like KD-Trees or Ball Trees to accelerate neighbor searches, reducing computational complexity from O(n²) to O(n log n). Adaptive Decision Thresholding: Implement automatic center selection by detecting "elbow points" in decision graphs using curvature analysis or gap statistics. Boundary Handling: Introduce density cutoff thresholds to filter noise points and handle ambiguous boundaries, often implemented through percentile-based density filtering.

### Testing Data Applications Synthetic Datasets: Validate algorithm performance on artificial data (spherical clusters, non-convex clusters) to test shape adaptability. UCI Datasets: Evaluate generalization capability and clustering accuracy using real-world data like Iris and Wine datasets with standard metrics (AMI, ARI).

The algorithm's strength lies in its automatic cluster detection, but it requires careful parameter tuning for density definitions and distance thresholds based on data characteristics. Practical implementations should include visualization tools for decision graphs and cluster validation metrics.