Data Mining: Multimedia, Soft Computing, and Bioinformatics
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Page vii
... Retrieval 1.5 Text Mining 1.6 Web Mining 1.7 Image Mining 1.8 Classification 1.9 Clustering 1.10 Rule Mining 1.11 String Matching 1.12 Bioinformatics 1.13 Data Warehousing 1.14 Applications and Challenges 1.15 Conclusions and Discussion ...
... Retrieval 1.5 Text Mining 1.6 Web Mining 1.7 Image Mining 1.8 Classification 1.9 Clustering 1.10 Rule Mining 1.11 String Matching 1.12 Bioinformatics 1.13 Data Warehousing 1.14 Applications and Challenges 1.15 Conclusions and Discussion ...
Page viii
... Image mining 66 2.4 Role of Neural Networks in Data Mining 67 2.4.1 Rule extraction 67 2.4.2 Rule evaluation 67 2.4.3 Clustering and self - organization 69 2.4.4 Regression 69 2.4.5 Information retrieval 69 2.5 Role of Genetic ...
... Image mining 66 2.4 Role of Neural Networks in Data Mining 67 2.4.1 Rule extraction 67 2.4.2 Rule evaluation 67 2.4.3 Clustering and self - organization 69 2.4.4 Regression 69 2.4.5 Information retrieval 69 2.5 Role of Genetic ...
Page xiii
... retrieval 322 9.2.3 Mathematical modeling of documents 323 9.2.4 Similarity - based matching for documents and queries 325 9.2.5 Latent semantic analysis 326 9.2.6 Soft computing approaches 9.3 Image Mining 9.3.1 Content - Based Image ...
... retrieval 322 9.2.3 Mathematical modeling of documents 323 9.2.4 Similarity - based matching for documents and queries 325 9.2.5 Latent semantic analysis 326 9.2.6 Soft computing approaches 9.3 Image Mining 9.3.1 Content - Based Image ...
Page xvi
... image compression , string matching , content based image retrieval , etc. , which can influence future developments in data mining , particularly for multimedia data mining . There are 10 chapters in the book . The first chapter ...
... image compression , string matching , content based image retrieval , etc. , which can influence future developments in data mining , particularly for multimedia data mining . There are 10 chapters in the book . The first chapter ...
Page xvii
... images and texts . Chapter 3 introduces the readers to the fundamentals of ... image mining , and Web mining issues . Next we introduce the readers to ... retrieval , and artificial intelli- gence , or it may be used as a reference ...
... images and texts . Chapter 3 introduces the readers to the fundamentals of ... image mining , and Web mining issues . Next we introduce the readers to ... retrieval , and artificial intelli- gence , or it may be used as a reference ...
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