Data Mining: Multimedia, Soft Computing, and Bioinformatics
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From inside the book
Page 1
... character rep- resentations only . The advanced database management technology of today is enabled to integrate different types of data , such as image , video , text , and other numeric as well as non - numeric data , in a provably ...
... character rep- resentations only . The advanced database management technology of today is enabled to integrate different types of data , such as image , video , text , and other numeric as well as non - numeric data , in a provably ...
Page 10
... characters . • The total amount of data spread over Internet sites is mind - boggling . Although the cost of storage has decreased drastically over the past decade due to significant advancement in the microelectronics and storage ...
... characters . • The total amount of data spread over Internet sites is mind - boggling . Although the cost of storage has decreased drastically over the past decade due to significant advancement in the microelectronics and storage ...
Page 11
... character strings ( e.g. , ' ze ' , ' th ' , ' ing ' ) are usually used repeatedly . It is also observed that the characters in an English text occur in a well - documented distribution , with letter " e " and " space " being the most ...
... character strings ( e.g. , ' ze ' , ' th ' , ' ing ' ) are usually used repeatedly . It is also observed that the characters in an English text occur in a well - documented distribution , with letter " e " and " space " being the most ...
Page 15
... characters , in addition to traditional data mining principles . We describe some of the classical string matching algorithms and their applications in Chapter 4 . In today's data processing environment , most of the text data is stored ...
... characters , in addition to traditional data mining principles . We describe some of the classical string matching algorithms and their applications in Chapter 4 . In today's data processing environment , most of the text data is stored ...
Page 21
... ... am and T = b1b2 ... b denote finite strings ( or sequences ) of characters ( or symbols ) over a finite alphabet Σ , where m , n are positive integers greater than 0. In its simplest form , the STRING MATCHING 21 1.11 String Matching.
... ... am and T = b1b2 ... b denote finite strings ( or sequences ) of characters ( or symbols ) over a finite alphabet Σ , where m , n are positive integers greater than 0. In its simplest form , the STRING MATCHING 21 1.11 String Matching.
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