Clustering Pathways Using Graph Mining Approach

1. Clustering PathwaysUsing Graph Mining Approach Mahmud Shahriar Hossain Monika Akbar Pramodh Pochu Venkata Sesha Sanagavarapu

2. Design Pipeline Graph Objects of Pathways STKE Dataset Preprocessor Frequent Subgraph Discovery Mined Data Pathway Clustering NN Search Pathway Relations

3. Dataset Properties (size)

4. Dataset Properties (size)

5. pf-ipf (tf-idf)

6. Dataset Properties (pf-ipf)

7. Dataset Properties (pf-ipf)

8. Subgraph Discovery • What so novel about pruning edges? min_sup=2%

9. Subgraph Discovery

10. Subgraph Discovery

11. Subgraph Discovery

12. Subgraph Discovery Overall attempts saved = 89.52% Overall time saved = 99.39%

13. Clustering

14. Clustering

15. Nearest Neighbors Cover Tree and Brute-force method

16. Pathway Relations (StoryTelling) p1 p7 p2 p8 S T p3 p9 • Bidirectional Search

17. Pathway Relations (StoryTelling)

18. Pathway Relations (StoryTelling)

19. Pathway Relations (StoryTelling)

20. Questions ???