Data Mining: Multimedia, Soft Computing, and Bioinformatics
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From inside the book
Page xiv
... Predicting protein secondary structure 382 10.6.2 Predicting protein tertiary structure 382 10.6.3 Determining binding sites 385 10.6.4 Classifying gene expression data 385 10.7 Conclusions and Discussion 386 References 387 Index About ...
... Predicting protein secondary structure 382 10.6.2 Predicting protein tertiary structure 382 10.6.3 Determining binding sites 385 10.6.4 Classifying gene expression data 385 10.7 Conclusions and Discussion 386 References 387 Index About ...
Page 1
... prediction . We can correlate this with a similar observation from the data and information domain . If the amount of information in the world doubles every 20 months , the size and number of databases probably increases at a similar ...
... prediction . We can correlate this with a similar observation from the data and information domain . If the amount of information in the world doubles every 20 months , the size and number of databases probably increases at a similar ...
Page 4
... predicting or clas- sifying the behavior of the model based on available data ) . In other words , it is an interdisciplinary field with a general goal of predicting outcomes and uncovering relationships in data [ 13 ] - [ 16 ] . It ...
... predicting or clas- sifying the behavior of the model based on available data ) . In other words , it is an interdisciplinary field with a general goal of predicting outcomes and uncovering relationships in data [ 13 ] - [ 16 ] . It ...
Page 7
... predict later events in the series . Data mining involves fitting models to or determining patterns from ob- served data . The fitted models play the role of inferred knowledge . Deciding whether the model reflects useful knowledge or ...
... predict later events in the series . Data mining involves fitting models to or determining patterns from ob- served data . The fitted models play the role of inferred knowledge . Deciding whether the model reflects useful knowledge or ...
Page 8
... prediction variable . 3. Clustering : This function maps a data item into one of several clusters , where clusters are natural groupings of data items based on similarity metrics or probability density models . 4. Rule generation : Here ...
... prediction variable . 3. Clustering : This function maps a data item into one of several clusters , where clusters are natural groupings of data items based on similarity metrics or probability density models . 4. Rule generation : Here ...
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