1. Research Topics Dr. Bernard Chen Ph.D. University of Central Arkansas Fall 2009

2. Research Topics Clustering-- Fuzzy-HKmeans clustering model for protein sequence motif discovery Using Biclustering algorithm to improve clustering results Association Rules – Super-rules clustering by positional association rule Positional Association Super-Rule with different mapping mechanism Classification -- Protein local 3D structure prediction incorporate with Chou-Fasman parameter

3. Future Works

4. Granular Computing Model Original dataset Fuzzy C-Means Clustering Informatio n Granule 1 Informatio n Granule M K-means Clustering K-means Clustering Join Information Final Sequence Motifs Information...

5. Hybrid Hierarchical K-means (HHK) clustering algorithm Number of cluster: 0

6. Hybrid Hierarchical K-means (HHK) clustering algorithm Number of cluster: 1

7. Hybrid Hierarchical K-means (HHK) clustering algorithm Number of cluster: 2

8. Hybrid Hierarchical K-means (HHK) clustering algorithm Number of cluster: 3

9. Hybrid Hierarchical K-means (HHK) clustering algorithm Number of cluster: 3

10. Hybrid Hierarchical K-means (HHK) clustering algorithm Number of cluster: 2

11. Hybrid Hierarchical K-means (HHK) clustering algorithm

13. Number of cluster: 3

14. Research Topics Clustering-- Fuzzy-HKmeans clustering model for protein sequence motif discovery Using Biclustering algorithm to improve clustering results Association Rules – Super-rules clustering by positional association rule Positional Association Super-Rule with different mapping mechanism Classification -- Protein local 3D structure prediction incorporate with Chou-Fasman parameter

15. Future Works

16. Research Topics Clustering-- Fuzzy-HKmeans clustering model for protein sequence motif discovery Using Biclustering algorithm to improve clustering results Association Rules – Super-rules clustering by positional association rule Positional Association Super-Rule with different mapping mechanism Classification -- Protein local 3D structure prediction incorporate with Chou-Fasman parameter

17. Future Works

18. Positional Association Rules

19. Positional Association Rules Example

20. Positional Association Rules D=>B minimum distance assurance = 60% 1. = 3/4 3.=1/4 2.= 1/4

21. Positional Association Rules B=>D minimum distance assurance = 60% 1. = 3/63. = 1/6 2.= 1/6

22. Positional Association Rules A=>B minimum distance assurance = 60% 1. = 2/43. = 1/4 2.= 1/4 4. = 1/4

23. Positional Association Rules A=>D minimum distance assurance = 60% 1. = 3/4 2.= 1/4

24. Positional Association Rules 2-itemset Positional Association Rules:

25. Research Topics Clustering-- Fuzzy-HKmeans clustering model for protein sequence motif discovery Using Biclustering algorithm to improve clustering results Association Rules – Super-rules clustering by positional association rule Positional Association Super-Rule with different mapping mechanism Classification -- Protein local 3D structure prediction incorporate with Chou-Fasman parameter

26. Future Works

27. Research Topics Clustering-- Fuzzy-HKmeans clustering model for protein sequence motif discovery Using Biclustering algorithm to improve clustering results Association Rules – Super-rules clustering by positional association rule Positional Association Super-Rule with different mapping mechanism Classification -- Protein local 3D structure prediction incorporate with Chou-Fasman parameter

28. Future Works

29. Research Topics Clustering-- Fuzzy-HKmeans clustering model for protein sequence motif discovery Using Biclustering algorithm to improve clustering results Association Rules – Super-rules clustering by positional association rule Positional Association Super-Rule with different mapping mechanism Classification -- Protein local 3D structure prediction incorporate with Chou- Fasman parameter Protein local 3D structure prediction incorporate with Voting Mechnism