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 104
... corresponding eigenvectors only . The eigenvector corresponding to the highest eigenvalue of FTF is called the first principal component . Likewise , the second principal component is the eigenvector corresponding to the next highest ...
... corresponding eigenvectors only . The eigenvector corresponding to the highest eigenvalue of FTF is called the first principal component . Likewise , the second principal component is the eigenvector corresponding to the next highest ...
Page 156
... corresponding pattern character pj . The symbol " Y " indicates occurrence of the pattern p ending at the text position i shown by a circle . The symbol " N " represents mismatch of t ; and corresponding pj . Whenever the result of ...
... corresponding pattern character pj . The symbol " Y " indicates occurrence of the pattern p ending at the text position i shown by a circle . The symbol " N " represents mismatch of t ; and corresponding pj . Whenever the result of ...
Page 304
... corresponding out- put node . Each outer level operator is modeled at the output layer by joining the corresponding hidden nodes . Note that a single attribute ( involving no inner level operators ) is directly connected to the ...
... corresponding out- put node . Each outer level operator is modeled at the output layer by joining the corresponding hidden nodes . Note that a single attribute ( involving no inner level operators ) is directly connected to the ...
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