Data Mining: Multimedia, Soft Computing, and BioinformaticsWhile the digital revolution has made huge volumes of high dimensional multimedia data available, it has also challenged users to extract the information they seek from heretofore unthinkably huge datasets. Traditional hard computing data mining techniques have concentrated on flat-file applications. Soft computing tools - such as fuzzy sets, artificial neural networks, genetic algorithms and rough sets - however, offer the opportunity to apply a wide range of data types to a variety of vital functions by handling real-life uncertainty with low-cost solutions. "Data Mining: Multimedia, Soft Computing, and Bioinformatics" provides an accessible introduction to fundamental and advanced data mining technologies. |
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Page 76
... Subnetworks ( SN3 ) Concatenation of Subnetworks ( SN2 ) Evolution of the Population of Concatenated networks with GA having variable mutation operator Feature Space Final Solution Network Fig . 2.9 Knowledge flow in a modular rough ...
... Subnetworks ( SN3 ) Concatenation of Subnetworks ( SN2 ) Evolution of the Population of Concatenated networks with GA having variable mutation operator Feature Space Final Solution Network Fig . 2.9 Knowledge flow in a modular rough ...
Page 302
... subnetwork modules are initially encoded , for each two - class sub - problem , from the dependency rules . These subnetworks are then combined and the final network is evolved using a GA with restricted mutation operator which utilizes ...
... subnetwork modules are initially encoded , for each two - class sub - problem , from the dependency rules . These subnetworks are then combined and the final network is evolved using a GA with restricted mutation operator which utilizes ...
Page 306
... subnetwork module ( fuzzy MLP ) . 3. Partially evolve each of the subnetworks using conventional GA . 4. Concatenate the subnetwork modules to obtain the complete network . For concatenation the intramodule links are left unchanged ...
... subnetwork module ( fuzzy MLP ) . 3. Partially evolve each of the subnetworks using conventional GA . 4. Concatenate the subnetwork modules to obtain the complete network . For concatenation the intramodule links are left unchanged ...
Contents
Soft Computing | 35 |
Multimedia Data Compression | 89 |
standard | 129 |
Copyright | |
8 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 attributes binary Bioinformatics bits C₁ categorical character chromosome classification coding coefficients color components content-based image retrieval corresponding data compression data mining database dataset datatypes decision tree decoder defined dictionary distance document domain encoded entropy entropy encoding evaluation example extracted feature frequent itemsets fuzzy sets gene Hence Huffman code IEEE Transactions image retrieval initial input interaction involving JPEG knowledge discovery learning linguistic matrix measure method Mitra multimedia data neural networks neuro-fuzzy neurons node objects optimal output parameters partition pattern matching pixel prediction problem protein quantization query representation represented result rough set S. K. Pal sample Section sequence shown in Fig soft computing spatial statistical string matching structure subbands subnetworks subsets substring symbol Table techniques text mining transformed vector visual wavelet Web mining weights