Data Mining: Multimedia, Soft Computing, and Bioinformatics
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Page viii
... Theory Concepts 3.2.1 Discrete memoryless model and entropy 3.2.2 Noiseless Source Coding Theorem Classification of Compression Algorithms 89 89 91 91 92 94 3.4 A Data Compression Model 95 3.5 Measures of Compression viii CONTENTS.
... Theory Concepts 3.2.1 Discrete memoryless model and entropy 3.2.2 Noiseless Source Coding Theorem Classification of Compression Algorithms 89 89 91 91 92 94 3.4 A Data Compression Model 95 3.5 Measures of Compression viii CONTENTS.
Page ix
... Measures of Compression Performance 96 3.5.1 Compression ratio and bits per sample 97 3.5.2 Quality metric 97 3.5.3 Coding complexity 99 3.6 Source Coding Algorithms 99 3.6.1 Run - length coding 99 3.6.2 Huffman coding 100 3.7 Principal ...
... Measures of Compression Performance 96 3.5.1 Compression ratio and bits per sample 97 3.5.2 Quality metric 97 3.5.3 Coding complexity 99 3.6 Source Coding Algorithms 99 3.6.1 Run - length coding 99 3.6.2 Huffman coding 100 3.7 Principal ...
Page xi
... Measures and Symbolic Objects 229 6.2.1 Numeric objects 229 6.2.2 Binary objects 229 6.2.3 Categorical objects 231 6.2.4 Symbolic objects 231 6.3.1 6.3 Clustering Categories Partitional clustering 232 232 6.3.2 Hierarchical clustering ...
... Measures and Symbolic Objects 229 6.2.1 Numeric objects 229 6.2.2 Binary objects 229 6.2.3 Categorical objects 231 6.2.4 Symbolic objects 231 6.3.1 6.3 Clustering Categories Partitional clustering 232 232 6.3.2 Hierarchical clustering ...
Page 7
... measure of interestingness of a pattern , namely , objective and subjective . The former uses the structure of the pattern and is generally quantitative . Often it fails to capture all the complexities of the pattern discovery process ...
... measure of interestingness of a pattern , namely , objective and subjective . The former uses the structure of the pattern and is generally quantitative . Often it fails to capture all the complexities of the pattern discovery process ...
Page 19
... measure is used for assessing the discriminatory power of the attributes at each level of the tree . 2. Probabilistic or generative models , which calculate probabilities for hy- potheses based on Bayes ' theorem [ 35 ] . 3. Nearest ...
... measure is used for assessing the discriminatory power of the attributes at each level of the tree . 2. Probabilistic or generative models , which calculate probabilities for hy- potheses based on Bayes ' theorem [ 35 ] . 3. Nearest ...
Contents
1 | |
2 Soft Computing | 35 |
3 Multimedia Data Compression | 89 |
4 String Matching | 143 |
5 Classification in Data Mining | 181 |
6 Clustering in Data Mining | 227 |
7 Association Rules | 267 |
8 Rule Mining with Soft Computing | 293 |
9 Multimedia Data Mining | 319 |
An Application | 365 |
Index | 392 |
About the Authors | 399 |
Other editions - View all
Data Mining: Multimedia, Soft Computing, and Bioinformatics Sushmita Mitra,Tinku Acharya No preview available - 2005 |
Common terms and phrases
analysis applications association rules attributes binary Bioinformatics bits categorical character chromosome classification coding coefficients color components content-based image retrieval corresponding data compression data mining dataset decision tree decoder defined dictionary distance document domain encoded entropy entropy encoding evaluation example extracted feature frequent itemsets fuzzy sets genetic algorithms Hence Huffman code IEEE 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 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 Transactions on Neural transformed vector visual wavelet Web mining weights