Data Mining: Multimedia, Soft Computing, and Bioinformatics
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Page 18
... categorical in nature . A numerical attribute has continu- ous , quantitative values . A categorical attribute , on the other hand , takes up discrete , symbolic values that can also be class labels or categories . If the de- pendent ...
... categorical in nature . A numerical attribute has continu- ous , quantitative values . A categorical attribute , on the other hand , takes up discrete , symbolic values that can also be class labels or categories . If the de- pendent ...
Page 228
... categorical clustering refers to the clustering of symbolic or categorical data . This is important from the point of view of data mining , where one has to mine for information from a set of symbolic objects . These objects are defined ...
... categorical clustering refers to the clustering of symbolic or categorical data . This is important from the point of view of data mining , where one has to mine for information from a set of symbolic objects . These objects are defined ...
Page 231
... Categorical objects Nominal ( or categorical ) variables are a generalization of binary variables , in that each can take up more than two states . For example , color can be { red , yellow , blue , green } . One can use simple matching ...
... Categorical objects Nominal ( or categorical ) variables are a generalization of binary variables , in that each can take up more than two states . For example , color can be { red , yellow , blue , green } . One can use simple matching ...
Contents
Soft Computing | 37 |
Multimedia Data Compression | 89 |
standard | 129 |
Copyright | |
9 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 ATATA binary Bioinformatics Boyer-Moore algorithm C₁ categorical classification clustering coding coefficients color components computational complexity content-based image retrieval corresponding data compression data mining database dataset decision tree decoder defined dictionary distance document domain efficient encoder outputs entropy entropy encoding evaluation example extracted feature fuzzy sets gene Hence Huffman code IEEE Transactions image retrieval initial input integer involving JPEG Karp-Rabin knowledge discovery knowledge-based network learning length linguistic matching algorithms matrix measure method mismatch Mitra multimedia data neural networks neuro-fuzzy neurons objects occurrence optimal partition pattern matching pixel prediction prefix protein pruning quantization query represented result rough set sample Section sequence shown in Fig soft computing split statistical string matching structure substring suffix symbol Table techniques text mining transformed vector wavelet Web mining weights