Data Mining: Multimedia, Soft Computing, and Bioinformatics
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Page xii
... knowledge - based networks 297 8.3 Modular Hybridization 302 8.3.1 Rough fuzzy MLP 302 8.3.2 Modular knowledge - based network 305 8.3.3 Evolutionary design 308 8.3.4 Rule extraction 310 Results 8.4 8.3.5 Conclusions and Discussion 311 ...
... knowledge - based networks 297 8.3 Modular Hybridization 302 8.3.1 Rough fuzzy MLP 302 8.3.2 Modular knowledge - based network 305 8.3.3 Evolutionary design 308 8.3.4 Rule extraction 310 Results 8.4 8.3.5 Conclusions and Discussion 311 ...
Page xvii
... based on soft computing and advanced signal processing techniques with recent developments . We deal with multimedia ... knowledge of statistics and probability theory . For the major part of this project we worked from the two ends of ...
... based on soft computing and advanced signal processing techniques with recent developments . We deal with multimedia ... knowledge of statistics and probability theory . For the major part of this project we worked from the two ends of ...
Page 4
... knowledge from data . The term KDD refers to the overall process of ... based on available data ) . In other words , it is an interdisciplinary ... Knowledge Discovery in Databases ( KDD ) has evolved , and continues to evolve , from the ...
... knowledge from data . The term KDD refers to the overall process of ... based on available data ) . In other words , it is an interdisciplinary ... Knowledge Discovery in Databases ( KDD ) has evolved , and continues to evolve , from the ...
Page 6
... knowledge : It includes incorporating this knowledge into the performance system and taking actions based on the knowledge . In other words , given huge volumes of heterogeneous data , the objective is to efficiently extract meaningful ...
... knowledge : It includes incorporating this knowledge into the performance system and taking actions based on the knowledge . In other words , given huge volumes of heterogeneous data , the objective is to efficiently extract meaningful ...
Page 9
... ( based on instances or features ) , learning from one or more of the selected subsets , and possibly combining the ... KNOWLEDGE DISCOVERY AND DATA MINING 9.
... ( based on instances or features ) , learning from one or more of the selected subsets , and possibly combining the ... KNOWLEDGE DISCOVERY AND DATA MINING 9.
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