Data Mining: Multimedia, Soft Computing, and Bioinformatics
|
From inside the book
Results 1-3 of 86
Page 22
... problem consists of searching the text T to find the occurrence ( s ) of the pattern P in T ( m≤ n ) . = Several variants of the basic problem can be considered . The pattern may consist of a finite set of sequences P { P1 , P2 ...
... problem consists of searching the text T to find the occurrence ( s ) of the pattern P in T ( m≤ n ) . = Several variants of the basic problem can be considered . The pattern may consist of a finite set of sequences P { P1 , P2 ...
Page 146
... problem is the problem of finding all the text locations where the given pattern p occurs in the given text t . The string matching problem has been depicted pictorially in Fig . 4.2 . Σ = { a , b , c ) Pattern ( p ) : bab . Position ...
... problem is the problem of finding all the text locations where the given pattern p occurs in the given text t . The string matching problem has been depicted pictorially in Fig . 4.2 . Σ = { a , b , c ) Pattern ( p ) : bab . Position ...
Page 177
... problem is to find the occurrence ( s ) of a pattern p in t by searching directly into the compressed text c ( t ) . The compressed pattern matching problem becomes even more challenging when the pattern is not fully specified , because ...
... problem is to find the occurrence ( s ) of a pattern p in t by searching directly into the compressed text c ( t ) . The compressed pattern matching problem becomes even more challenging when the pattern is not fully specified , because ...
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