Data Mining: Multimedia, Soft Computing, and Bioinformatics

Front Cover
Wiley, Sep 25, 2003 - Computers - 424 pages
0 Reviews
A primer on traditional hard and emerging soft computing approaches for mining multimedia data

While the digital revolution has made huge volumes of high dimensional multimedia data available, it has also challenged users to extract the information they seek from heretofore unthinkably huge datasets. Traditional hard computing data mining techniques have concentrated on flat-file applications. Soft computing tools-such as fuzzy sets, artificial neural networks, genetic algorithms, and rough sets-however, offer the opportunity to apply a wide range of data types to a variety of vital functions by handling real-life uncertainty with low-cost solutions. Data Mining: Multimedia, Soft Computing, and Bioinformatics provides an accessible introduction to fundamental and advanced data mining technologies.

This readable survey describes data mining strategies for a slew of data types, including numeric and alpha-numeric formats, text, images, video, graphics, and the mixed representations therein. Along with traditional concepts and functions of data mining-like classification, clustering, and rule mining-the authors highlight topical issues in multimedia applications and bioinformatics. Principal topics discussed throughout the text include: The role of soft computing and its principles in data mining Principles and classical algorithms on string matching and their role in data (mainly text) mining Data compression principles for both lossless and lossy techniques, including their scope in data mining Access of data using matching pursuits both in raw and compressed data domains Application in mining biological databases

From inside the book

 

Results 1-3 of 52

Page 106
3.5 DPCM showing (a) neighbors of pixel X to be encoded, (b) prediction function
, and (c) block diagram. of the pixel is similar to the actual value. But in reality,
using an effective prediction model, we can predict a pixel value which is very ...
Page 131
A dynamic dictionary coding, however, builds a dictionary dynamically using the
message itself that is being encoded or decoded. The basic idea behind most of
the dynamic dictionary-based robust lossless text compression schemes is to first
 ...
Page 132
In this approach a fixed-size buffer, containing the previously encoded character
sequence that precedes the current coding position, can be considered as a
dictionary. The encoder matches the input sequence through a sliding window, ...

Where's the rest of this book?

What people are saying - Write a review

We haven't found any reviews in the usual places.

Contents

Soft Computing
35
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

analysis applications approach association rules attributes binary Bioinformatics bits categorical character chromosome classification coding coefficients color components content-based image retrieval corresponding data compression data mining database dataset datatypes decision tree decoder dictionary distance document domain encoded entropy entropy encoding evaluation example extracted feature frequent itemsets fuzzy sets gene Hence Huffman code IEEE Transactions image retrieval initial input interaction involving JPEG knowledge discovery knowledge-based network 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 result rough sets 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 transformed vector visual wavelet Web mining weights

References to this book

About the author (2003)

SUSHMITA MITRA, PHD, is a Professor at Machine Intelligence Unit, Indian Statistical Institute, in Calcutta. She is a coauthor of Neuro-Fuzzy Pattern Recognition: Methods in Soft Computing, also published by Wiley.

TINKU ACHARYA, PHD, Senior Executive vice president and Chief Science Officer of Avisere Inc., Tucson, Arizona, is involved in multimedia data mining applications. He is also an adjunct professor in the Department of Electrical Engineering at Arizona State University. He was recognized as the Most Prolific Inventor of Intel Corporation Worldwide in 1999.

Bibliographic information

QR code for Data Mining
TitleData Mining: Multimedia, Soft Computing, and Bioinformatics
AuthorsSushmita Mitra, Tinku Acharya
Editionillustrated
PublisherWiley, 2003
Original fromthe University of Michigan
Digitized19 Nov 2007
ISBN0471460540, 9780471460541
Length424 pages
Subjects › 

Computers / Computer Engineering
Technology & Engineering / Electrical
  
Export CitationBiBTeX EndNote RefMan