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A novel approach based on prototypes and rough sets for document and feature reductions  in text categorization Shing-Hua Ho and Jung-Hsien Chiang Reporter :CHE-MIN LIAO 2007/8/27
Outline ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Introduction ,[object Object],[object Object]
Introduction ,[object Object],[object Object],[object Object]
Introduction ,[object Object],[object Object],[object Object]
Document reduction based on prototype concept ,[object Object],[object Object]
Document reduction based on prototype concept ,[object Object],[object Object],[object Object]
Document reduction based on prototype concept ,[object Object],[object Object]
Document reduction based on prototype concept ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Document reduction based on prototype concept Step 05: Determine the index of the closest prototype to each document  dv  as  Iv =arg min( Dvt ) Step 06: If  Iv = z , ∀ dv ∈ Gz  Then   go to Step 11 End If Step 07: If  s ( PIv )≠ s ( Pz ) ∀ cv ∈ Gz  Then   Set  Z = Z +1 and split  Gz  into two subgroups   Ga  and  Gb   Update their means:  Pa =mean( Ga ) and   Pb =mean( Gb )   If  s ( Pa )= s ( Pb ) Then   go to Step 04   End If End If
Document reduction based on prototype concept ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Feature reduction based on rough sets ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Feature reduction based on rough sets ,[object Object]
Feature reduction based on rough sets
Feature reduction based on rough sets
Feature reduction based on rough sets
Feature reduction based on rough sets
Feature reduction based on rough sets The rough-based feature selection algorithm achieves exclusive clusters and required to determine the desired number of clusters.Theoretically,the suitable maximum number of clusters is estimated as  ,where N is the size of the features
Feature reduction based on rough sets
Performance evaluation and comparison
Performance evaluation and comparison ,[object Object],[object Object],[object Object],[object Object],[object Object]
Performance evaluation and comparison ,[object Object],[object Object],[object Object],[object Object],[object Object]
Experimental design and result analysis ,[object Object],[object Object]
Experimental design and result analysis
Experimental design and result analysis
Experimental design and result analysis ,[object Object],[object Object]
Experimental design and result analysis
Experimental design and result analysis
Conclusion ,[object Object],[object Object]

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