Cover of: Pattern recognition algorithms for data mining | Sankar K Pal

Pattern recognition algorithms for data mining

scalability, knowledge discovery and soft granular computing
  • 244 Pages
  • 0.47 MB
  • 2170 Downloads
  • English
by
Chapman & Hall/CRC , Boca Raton, Fla
Data mining, Pattern recognition systems, Computer algorithms, Granular comp
StatementSankar K. Pal and Pabitra Mitra
ContributionsMitra, Pabitra, PhD
Classifications
LC ClassificationsQA76.9.D343 P38 2004
The Physical Object
Paginationxxix, 244 p. :
ID Numbers
Open LibraryOL17140222M
ISBN 101584884576
LC Control Number2004043539

Pattern Recognition Algorithms for Data Mining addresses different pattern recognition (PR) tasks in a unified framework with both theoretical and experimental results. Tasks covered include data condensation, feature selection, case generation, clustering/classification, and rule.

Naturally, the data mining and pattern recognition repertoire is quite limited: I have chosen problem areas that are well suited for linear algebra techniques.

In order to use intelligently the powerful software for computing matrix decompositions available in MATLAB, etc., some understanding of the underlying algorithms is necessary. An accompanying book with Matlab code of the most common methods and algorithms in the book, together with a descriptive summary and solved examples, and including real-life data sets in imaging and audio recognition.

The companion book is available separately or at a special packaged price (Book ISBN: Package ISBN: ). A new approach to the issue of data quality in pattern recognition Detailing foundational Pattern recognition algorithms for data mining book before introducing more complex methodologies and algorithms, this book is a self-contained manual for advanced data analysis and data mining.

Top-down organization presents detailed applications only after methodological issues have been mastered, and step-by-step. Pattern Recognition Algorithms for Data Mining addresses different pattern recognition (PR) tasks in a unified framework with both theoretical and experimental results.

Tasks covered include data condensation, feature selection, case generation, clustering/classification, and rule 1/5(1). Pattern Recognition Algorithms for Data Mining addresses different pattern recognition (PR) tasks in a unified framework with both theoretical and experimental results.

Tasks covered include data condensation, feature selection, case generation, clustering/classification, and rule generation and evaluation. This volume presents various 3/5(1).

Description Pattern recognition algorithms for data mining EPUB

Sequential pattern mining is a topic of data mining concerned with finding statistically relevant patterns between data examples where the values are delivered in a sequence. It is usually presumed that the values are discrete, and thus time series mining is closely related, but usually considered a different activity.

Sequential pattern mining is a special case of structured data. An accompanying book with Matlab code of the most common methods and algorithms in the book, together with a descriptive summary and solved examples, and including real-life data sets in imaging and audio recognition.

"This book is an excellent reference for pattern recognition, machine learning, and data mining. It focuses on the problems. Pattern Recognition and Big Data provides state-of-the-art classical and modern approaches to pattern recognition and mining, with extensive real life applications.

The book describes efficient soft and robust machine learning algorithms and granular computing techniques for data mining and knowledge discovery; and the issues associated with. This application-oriented book describes how modern matrix methods can be used to solve problems in data mining and pattern recognition, gives an introduction to pattern recognition algorithms for data mining addresses different pattern recognition pr tasks in a unified framework with both theoretical and experimental results contents in this course 6 different.

the book is application oriented, it is not possible to give a comprehensive treatment of the mathematical and numerical aspects of the linear algebra algorithms used. The book has three parts. After a short introduction to a couple of areas of data mining and pattern recognition, linear algebra concepts and matrix decom-positions are presented.

"Pattern Recognition Algorithms in Data Mining is a book that commands admiration. Its authors, Professors S.K. Pal and P. Mitra are foremost authorities in pattern recognition, data mining, and related fields/5(3). "Pattern Recognition Algorithms in Data Mining is a book that commands admiration.

Its authors, Professors S.K. Pal and P. Mitra are foremost authorities in pattern recognition, data mining, and related fields. Within its covers, the reader finds an exceptionally well-organized exposition of every concept and every method that is of relevance Reviews: 1.

This book constitutes the refereed proceedings of the 11th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDMheld in Hamburg, Germany, in July The 41 full papers presented were carefully reviewed and. This book includes a free, day trial copy of Pattern Recognition Workbench, a powerful, easy-to-use system that combines machine learning, neural networks, and statistical algorithms to help you apply pattern recognition to your data right : $ Author by: Earl Gose Languange: en Publisher by: Prentice Hall Format Available: PDF, ePub, Mobi Total Read: 63 Total Download: File Size: 42,9 Mb Description: Over the past 20 to 25 years, pattern recognition has become an important part of image processing applications where the input data is an book is a complete introduction to pattern recognition.

Pattern recognition is a fast growing area with applications in a widely diverse number of fields such as communications engineering, bioinformatics, data mining, content-based database retrieval, to name but a few.

Data mining is mostly about finding relevant features or patterns in a particular data, this can be achieved using machine learning especially unsupervised learning algorithms such as clustering. Data mining is mainly about trying to find a human.

The book is suitable for courses on machine learning, statistics, computer science, signal processing, computer vision, data mining, and bioinformatics. Extensive support is provided for course instructors, including more than exercises, graded according to difficulty.

4 Chapter 1. Vectors and Matrices in Data Mining and Pattern Recognition Vectors and Matrices The following examples illustrate the use of vectors and matrices in data mining.

Details Pattern recognition algorithms for data mining FB2

These examples present the main data mining areas discussed in the book, and they will be described in more detail in Part II. Apply pattern recognition to find the hidden gems in your data. Data mining technology is helping businesses everywhere to work smarter by revealing unknown patterns within existing archives.

Applying the latest advances in pattern recognition software can give you a key competitive edge across all data mining applications.

The tutorials and software package included in Solving. Book Description. Pattern Recognition Algorithms for Data Mining addresses different pattern recognition (PR) tasks in a unified framework with both theoretical and experimental results.

Tasks covered include data condensation, feature selection, case generation, clustering/classification, and rule generation and evaluation. Pattern Recognition Algorithms for Data Mining: Scalability, Knowledge Discovery, and Soft Granular Computing May Book Description.

This application-oriented book describes how modern matrix methods can be used to solve problems in data mining and pattern recognition, gives an introduction to matrix theory and decompositions, and provides students with a set of tools that can be modified for a particular application/5(6).

This book constitutes the refereed proceedings of the 9th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDMheld in New York, USA in July The 51 revised full papers presented were carefully reviewed and selected from.

Data Mining in Pattern Recognition Petra Perner Azriel Rosenfeld, A catalog record for this book is available from the Library of Congress. the topic of. Pattern Recognition Algorithms for Data Mining addresses different pattern recognition (PR) tasks in a unified framework with both theoretical and experimental results.

Download Pattern recognition algorithms for data mining FB2

Tasks covered include data condensation, feature selection, case generation, clustering/classification, and rule generation and evaluation.

This volume presents various theories, methodologies, and algorithms, using. I really recommend this one. Data Mining: Practical Machine Learning Tools and Techniques.

It seems the cover changed I have the green version with a chameleon. Anyway I was a total noob on Data Mining and ML before that book (still am I just ha. Matrix Methods in Data Mining and Pattern Recognition - Ebook written by Lars Elden. Read this book using Google Play Books app on your PC, android, iOS devices.

Download for offline reading, highlight, bookmark or take notes while you read Matrix Methods in Data Mining and Pattern : Lars Elden. Matrix Methods in Data Mining and Pattern Recognition (Fundamentals of Algorithms) April April Read More.

Author: Matrix Methods in Data Mining and Pattern Recognition (Fundamentals of Algorithms) Computing methodologies. Machine learning This book is the exception that carefully explains one of the fundamental papers of the. Book /2/23 page 8 8 chapter 1 vectors and matrices in data mining and pattern recognition linear algebra, with the emphasis on data mining and pattern recognition pattern recognition, machine learning, and data mining pattern recognition pattern recognition is the study of methods and algorithms for putting data objects into pattern.The book lays the foundations of data analysis, pattern mining, clustering, classification and regression, with a focus on the algorithms and the underlying algebraic, geometric, and probabilistic concepts.

New to this second edition is an entire part devoted to regression methods, including neural networks and deep learning.I am totally new in this field of DataMining and text based pattern recognition. Will really appreciate if anyone could suggest how to go ahead with pattern recognition algorithm from this plain text in my database to provide feed to my separate visual charts API.