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

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

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

Future Works

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...

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

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

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

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

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

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

Hybrid Hierarchical K-means (HHK) clustering algorithm

Number of cluster: 3

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

Future Works

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

Future Works

Positional Association Rules

Positional Association Rules Example

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

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

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

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

Positional Association Rules 2-itemset Positional Association Rules:

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

Future Works

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

Future Works

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