Data Mining: Multimedia, Soft Computing, and Bioinformatics
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Page 101
... nodes No and N3 , respectively . The new node Ng becomes the parent of N3 and N6 . ) 3. Label the probability of this new parent node as the sum of the proba- bilities of its two child nodes . ( Example : The new node Ng is now labeled ...
... nodes No and N3 , respectively . The new node Ng becomes the parent of N3 and N6 . ) 3. Label the probability of this new parent node as the sum of the proba- bilities of its two child nodes . ( Example : The new node Ng is now labeled ...
Page 190
... node are given in Tables 5.3 and 5.4 corresponding to attributes age and car type , respectively . Table 5.3 Initial numeric attribute list for root node age risk RID 17 high 1 20 high 5 23 high 0 32 low 4 43 high 2 68 low 3 Table 5.4 ...
... node are given in Tables 5.3 and 5.4 corresponding to attributes age and car type , respectively . Table 5.3 Initial numeric attribute list for root node age risk RID 17 high 1 20 high 5 23 high 0 32 low 4 43 high 2 68 low 3 Table 5.4 ...
Page 238
... node is represented by a set of c medoids . Two nodes are termed as neighbors if they only differ by one medoid . Hence each node has c✶ ( N −c ) neighbors . The main steps are as follows : • Initially , a node of c medoids is chosen ...
... node is represented by a set of c medoids . Two nodes are termed as neighbors if they only differ by one medoid . Hence each node has c✶ ( N −c ) neighbors . The main steps are as follows : • Initially , a node of c medoids is chosen ...
Contents
Soft Computing | 37 |
Multimedia Data Compression | 89 |
standard | 129 |
Copyright | |
9 other sections not shown
Other editions - View all
Data Mining: Multimedia, Soft Computing, and Bioinformatics Sushmita Mitra,Tinku Acharya Limited preview - 2005 |
Data Mining: Multimedia, Soft Computing, and Bioinformatics Sushmita Mitra,Tinku Acharya No preview available - 2005 |
Common terms and phrases
applications approach association rules ATATA binary Bioinformatics Boyer-Moore algorithm C₁ categorical classification clustering coding coefficients color components computational complexity content-based image retrieval corresponding data compression data mining database dataset decision tree decoder defined dictionary distance document domain efficient encoder outputs entropy entropy encoding evaluation example extracted feature fuzzy sets gene Hence Huffman code IEEE Transactions image retrieval initial input integer involving JPEG Karp-Rabin knowledge discovery knowledge-based network learning length linguistic matching algorithms matrix measure method mismatch Mitra multimedia data neural networks neuro-fuzzy neurons objects occurrence optimal partition pattern matching pixel prediction prefix protein pruning quantization query represented result rough set sample Section sequence shown in Fig soft computing split statistical string matching structure substring suffix symbol Table techniques text mining transformed vector wavelet Web mining weights