Data Mining: Multimedia, Soft Computing, and Bioinformatics
|
From inside the book
Results 6-10 of 86
Page 18
... Section 9.3 . 1.8 CLASSIFICATION Classification is also described as supervised learning [ 35 ] . Let there be a database of tuples , each assigned a class label . The objective is to develop a model or profile for each class . An ...
... Section 9.3 . 1.8 CLASSIFICATION Classification is also described as supervised learning [ 35 ] . Let there be a database of tuples , each assigned a class label . The objective is to develop a model or profile for each class . An ...
Page 19
... Section 2.2.3 , as a major soft computing tool . We have devoted the whole of Chapter 5 to the principles and techniques for classification . 1.9 CLUSTERING A cluster is a collection of data objects which are similar to one another ...
... Section 2.2.3 , as a major soft computing tool . We have devoted the whole of Chapter 5 to the principles and techniques for classification . 1.9 CLUSTERING A cluster is a collection of data objects which are similar to one another ...
Page 21
... section , we briefly introduce the string matching problem [ 24 ] . Let Pa1a2 ... 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 ...
... section , we briefly introduce the string matching problem [ 24 ] . Let Pa1a2 ... 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 ...
Page 36
You have reached your viewing limit for this book.
You have reached your viewing limit for this book.
Page 37
You have reached your viewing limit for this book.
You have reached your viewing limit for this book.
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