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DBSCAN Clustering Pro
Density-based spatial clustering with comprehensive analysis tools
DBSCAN Parameters
Maximum distance between two points to be considered neighbors
Minimum number of points required to form a dense region (cluster)
💡 Parameter Tips:
- • Larger epsilon → Fewer, larger clusters
- • Smaller epsilon → More clusters or noise
- • Higher MinPts → Denser clusters required
- • Lower MinPts → More points clustered
Add Data Points
Dataset: My Dataset
Points: 9
Data Points (9)
| # | X | Y | Action |
|---|---|---|---|
| 1 | 1.00 | 1.00 | |
| 2 | 1.50 | 2.00 | |
| 3 | 2.00 | 1.50 | |
| 4 | 5.00 | 5.00 | |
| 5 | 5.50 | 6.00 | |
| 6 | 6.00 | 5.50 | |
| 7 | 9.00 | 2.00 | |
| 8 | 9.50 | 2.50 | |
| 9 | 10.00 | 2.00 |
📊 Clustering Results
Clusters Found
3
Core Points
9
Border Points
0
Noise Points
0
Cluster Breakdown:
Cluster 1: 3 points
Cluster 2: 3 points
Cluster 3: 3 points
Related Topics & Algorithms
Explore these related algorithms and concepts to deepen your understanding and discover complementary techniques.
Clustering
K-Means
Centroid-based clustering alternative
Dimensionality Reduction
PCA
Dimensionality reduction for visualization
Preprocessing
Feature Engineering
Feature preprocessing for clustering
Classification
KNN
Distance-based supervised learning
Deep Learning
Autoencoder
Learn representations for clustering
Fundamentals
Statistics
Statistical validation of clusters