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CB-DBSCAN: A Novel Clustering Algorithm for Adjacent Clusters with Different Densities Gashin Ghazizadeh Outline Introduction Multi-density Clustering CB-DBSCAN Results CB-DBSCAN: A Novel Clustering Algorithm for Adjacent

  1. CB-DBSCAN: A Novel Clustering Algorithm for Adjacent Clusters with Different Densities Gashin Ghazizadeh

  2. Outline • Introduction • Multi-density Clustering • CB-DBSCAN • Results CB-DBSCAN: A Novel Clustering Algorithm for Adjacent Clusters 2020-05-04 2 with Different Densities | Ghazizadeh et al.

  3. Introduction • Clustering • Machine Learning algorithms to find groups of similar data points in data • Density-based and center-based algorithms • DBSCAN is a clustering algorithm that tries to find clusters based on the density of different regions of the data. CB-DBSCAN: A Novel Clustering Algorithm for Adjacent Clusters 2020-05-04 3 with Different Densities | Ghazizadeh et al.

  4. Multi-density Clustering CB-DBSCAN: A Novel Clustering Algorithm for Adjacent Clusters 2020-05-04 4 with Different Densities | Ghazizadeh et al.

  5. Multi-density Clustering CB-DBSCAN: A Novel Clustering Algorithm for Adjacent Clusters 2020-05-04 4 with Different Densities | Ghazizadeh et al.

  6. Multi-density Clustering 𝑫 𝟑 𝑫 𝟐 CB-DBSCAN: A Novel Clustering Algorithm for Adjacent Clusters 2020-05-04 4 with Different Densities | Ghazizadeh et al.

  7. Multi-density Clustering 𝑫 𝟑 𝑫 𝟐 CB-DBSCAN: A Novel Clustering Algorithm for Adjacent Clusters 2020-05-04 4 with Different Densities | Ghazizadeh et al.

  8. Multi-density Clustering 𝑫 𝟑 𝑫 𝟐 CB-DBSCAN: A Novel Clustering Algorithm for Adjacent Clusters 2020-05-04 4 with Different Densities | Ghazizadeh et al.

  9. Multi-density Clustering Core point 𝑫 𝟑 𝑫 𝟐 CB-DBSCAN: A Novel Clustering Algorithm for Adjacent Clusters 2020-05-04 4 with Different Densities | Ghazizadeh et al.

  10. Multi-density Clustering Core point 𝑫 𝟑 𝑫 𝟐 CB-DBSCAN: A Novel Clustering Algorithm for Adjacent Clusters 2020-05-04 4 with Different Densities | Ghazizadeh et al.

  11. Multi-density Clustering Core point 𝑫 𝟑 𝑫 𝟐 Border Point CB-DBSCAN: A Novel Clustering Algorithm for Adjacent Clusters 2020-05-04 4 with Different Densities | Ghazizadeh et al.

  12. Multi-density Clustering Core point 𝑫 𝟑 𝑫 𝟐 Border Point CB-DBSCAN: A Novel Clustering Algorithm for Adjacent Clusters 2020-05-04 4 with Different Densities | Ghazizadeh et al.

  13. Multi-density Clustering Core point 𝑫 𝟑 𝑫 𝟐 Noise Border Point CB-DBSCAN: A Novel Clustering Algorithm for Adjacent Clusters 2020-05-04 4 with Different Densities | Ghazizadeh et al.

  14. Multi-density Clustering Core point 𝑫 𝟑 𝑫 𝟐 Noise Border Point CB-DBSCAN: A Novel Clustering Algorithm for Adjacent Clusters 2020-05-04 4 with Different Densities | Ghazizadeh et al.

  15. Multi-density Clustering Boundary Point Core point 𝑫 𝟑 𝑫 𝟐 Noise Border Point CB-DBSCAN: A Novel Clustering Algorithm for Adjacent Clusters 2020-05-04 4 with Different Densities | Ghazizadeh et al.

  16. CB-DBSCAN • Clustering with DBSCAN • Merging • Noise points • Parameter ( Cdis and Ddis ) CB-DBSCAN: A Novel Clustering Algorithm for Adjacent Clusters 2020-05-04 5 with Different Densities | Ghazizadeh et al.

  17. Mini-Clusters How to choose correct parameters? CB-DBSCAN: A Novel Clustering Algorithm for Adjacent Clusters 2020-05-04 6 with Different Densities | Ghazizadeh et al.

  18. Mini-Clusters How to choose correct parameters? CB-DBSCAN: A Novel Clustering Algorithm for Adjacent Clusters 2020-05-04 6 with Different Densities | Ghazizadeh et al.

  19. Mini-Clusters How to choose correct parameters? CB-DBSCAN: A Novel Clustering Algorithm for Adjacent Clusters 2020-05-04 6 with Different Densities | Ghazizadeh et al.

  20. Mini-Clusters How to choose correct parameters? CB-DBSCAN: A Novel Clustering Algorithm for Adjacent Clusters 2020-05-04 6 with Different Densities | Ghazizadeh et al.

  21. Mini-Clusters How to choose correct parameters? CB-DBSCAN: A Novel Clustering Algorithm for Adjacent Clusters 2020-05-04 6 with Different Densities | Ghazizadeh et al.

  22. Mini-Clusters How to choose correct parameters? CB-DBSCAN: A Novel Clustering Algorithm for Adjacent Clusters 2020-05-04 6 with Different Densities | Ghazizadeh et al.

  23. Merging CB-DBSCAN: A Novel Clustering Algorithm for Adjacent Clusters 2020-05-04 7 with Different Densities | Ghazizadeh et al.

  24. Merging CB-DBSCAN: A Novel Clustering Algorithm for Adjacent Clusters 2020-05-04 7 with Different Densities | Ghazizadeh et al.

  25. Merging CB-DBSCAN: A Novel Clustering Algorithm for Adjacent Clusters 2020-05-04 7 with Different Densities | Ghazizadeh et al.

  26. Merging CB-DBSCAN: A Novel Clustering Algorithm for Adjacent Clusters 2020-05-04 7 with Different Densities | Ghazizadeh et al.

  27. Merging CB-DBSCAN: A Novel Clustering Algorithm for Adjacent Clusters 2020-05-04 7 with Different Densities | Ghazizadeh et al.

  28. Merging (cont.) ▪ No additional parameters (3*Ddis and Cdis/2) ▪ To combine boundary points and center points of clusters CB-DBSCAN: A Novel Clustering Algorithm for Adjacent Clusters 2020-05-04 8 with Different Densities | Ghazizadeh et al.

  29. Results F-measure Comparison of Clustering Algorithms DBSCAN CB-DBSCAN PACA-DBSCAN kAA-DBSCAN OPTICS Flame 0.89 0.99 0.98 0.98 0.91 Seeds 0.56 0.90 0.72 0.85 0.8 Path based 0.66 0.91 0.92 0.99 0.69 Breast 0.61 0.97 0.75 0.73 0.66 Wine 0.67 0.94 0.72 0.72 0.71 CB-DBSCAN: A Novel Clustering Algorithm for Adjacent Clusters 2020-05-04 9 with Different Densities | Ghazizadeh et al.

  30. Results (cont.) CB-DBSCAN and DBSCAN on Twitter data Accuracy F-Measure Silhouette Coefficient DBSCAN 0.91 0.47 0.38 CB-DBSCAN 0.98 0.65 0.51 CB-DBSCAN: A Novel Clustering Algorithm for Adjacent Clusters 2020-05-04 10 with Different Densities | Ghazizadeh et al.

  31. ! N U T H A K Y O CB-DBSCAN: A Novel Clustering Algorithm for Adjacent Clusters 2020-05-04 11 with Different Densities | Ghazizadeh et al.

  32. Y O U THA N ! K CB-DBSCAN: A Novel Clustering Algorithm for Adjacent Clusters 2020-05-04 11 with Different Densities | Ghazizadeh et al.

  33. References • Ester, M., Kriegel, H. P., Sander, J., & Xu, X. (1996, August). Density-based spatial clustering of applications with noise. In Int. Conf. Knowledge Discovery and Data Mining (Vol. 240, p. 6). • Kim, J. H., Choi, J. H., Yoo, K. H., & Nasridinov, A. (2019). AA-DBSCAN: an approximate adaptive DBSCAN for finding clusters with varying densities. The Journal of Supercomputing , 75 (1), 142-169. • Jiang, H., Li, J., Yi, S., Wang, X., & Hu, X. (2011). A new hybrid method based on partitioning-based DBSCAN and ant clustering. Expert Systems with Applications , 38 (8), 9373-9381. • Ankerst, M., Breunig, M. M., Kriegel, H. P., & Sander, J. (1999). OPTICS: ordering points to identify the clustering structure. ACM Sigmod record , 28 (2), 49-60. CB-DBSCAN: A Novel Clustering Algorithm for Adjacent Clusters 2020-05-04 12 with Different Densities | Ghazizadeh et al.

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