Applied Biclustering Methods For Big And High Dimensional Data Using R

Applied Biclustering Methods for Big and High Dimensional Data Using R
Publisher CRC Press
Release Date
Category Mathematics
Total Pages 433
ISBN 9781315356396
Rating 4/5 from 21 reviews
GET BOOK

Proven Methods for Big Data Analysis As big data has become standard in many application areas, challenges have arisen related to methodology and software development, including how to discover meaningful patterns in the vast amounts of data. Addressing these problems, Applied Biclustering Methods for Big and High-Dimensional Data Using R shows how to apply biclustering methods to find local patterns in a big data matrix. The book presents an overview of data analysis using biclustering methods from a practical point of view. Real case studies in drug discovery, genetics, marketing research, biology, toxicity, and sports illustrate the use of several biclustering methods. References to technical details of the methods are provided for readers who wish to investigate the full theoretical background. All the methods are accompanied with R examples that show how to conduct the analyses. The examples, software, and other materials are available on a supplementary website.

Applied Biclustering Methods for Big and High Dimensional Data Using R
  • Author : Adetayo Kasim,Ziv Shkedy,Sebastian Kaiser,Sepp Hochreiter,Willem Talloen
  • Publisher : CRC Press
  • Release Date : 2016-08-18

Proven Methods for Big Data Analysis As big data has become standard in many application areas, challenges have arisen related to methodology and software development, including how to discover meaningful patterns in the vast amounts of data. Addressing these problems, Applied Biclustering Methods for Big and High-Dimensional Data Using R

GET BOOK
Biopharmaceutical Applied Statistics Symposium
  • Author : Karl E. Peace,Ding-Geng Chen,Sandeep Menon
  • Publisher : Springer
  • Release Date : 2018-09-03

This BASS book Series publishes selected high-quality papers reflecting recent advances in the design and biostatistical analysis of biopharmaceutical experiments – particularly biopharmaceutical clinical trials. The papers were selected from invited presentations at the Biopharmaceutical Applied Statistics Symposium (BASS), which was founded by the first Editor in 1994 and has since become

GET BOOK
Clinical Trial Optimization Using R
  • Author : Alex Dmitrienko,Erik Pulkstenis
  • Publisher : CRC Press
  • Release Date : 2017-08-10

Clinical Trial Optimization Using R explores a unified and broadly applicable framework for optimizing decision making and strategy selection in clinical development, through a series of examples and case studies. It provides the clinical researcher with a powerful evaluation paradigm, as well as supportive R tools, to evaluate and select

GET BOOK
Market Segmentation Analysis
  • Author : Sara Dolnicar,Bettina Grün,Friedrich Leisch
  • Publisher : Springer
  • Release Date : 2018-07-20

This book is published open access under a CC BY 4.0 license. This open access book offers something for everyone working with market segmentation: practical guidance for users of market segmentation solutions; organisational guidance on implementation issues; guidance for market researchers in charge of collecting suitable data; and guidance for data

GET BOOK
Statistical Learning with Sparsity
  • Author : Trevor Hastie,Robert Tibshirani,Martin Wainwright
  • Publisher : CRC Press
  • Release Date : 2015-05-07

Discover New Methods for Dealing with High-Dimensional Data A sparse statistical model has only a small number of nonzero parameters or weights; therefore, it is much easier to estimate and interpret than a dense model. Statistical Learning with Sparsity: The Lasso and Generalizations presents methods that exploit sparsity to help

GET BOOK
Co Clustering
  • Author : Gérard Govaert,Mohamed Nadif
  • Publisher : John Wiley & Sons
  • Release Date : 2013-12-11

Cluster or co-cluster analyses are important tools in a variety ofscientific areas. The introduction of this book presents a state ofthe art of already well-established, as well as more recent methodsof co-clustering. The authors mainly deal with the two-modepartitioning under different approaches, but pay particularattention to a probabilistic approach. Chapter 1

GET BOOK
Modeling Dose Response Microarray Data in Early Drug Development Experiments Using R
  • Author : Dan Lin,Ziv Shkedy,Daniel Yekutieli,Dhammika Amaratunga,Luc Bijnens
  • Publisher : Springer Science & Business Media
  • Release Date : 2012-08-27

This book focuses on the analysis of dose-response microarray data in pharmaceutical settings, the goal being to cover this important topic for early drug development experiments and to provide user-friendly R packages that can be used to analyze this data. It is intended for biostatisticians and bioinformaticians in the pharmaceutical

GET BOOK
Plaid Models for Gene Expression Data
  • Author : Laura Lazzeroni,Art B. Owen
  • Publisher : Unknown
  • Release Date : 2000

Read online Plaid Models for Gene Expression Data written by Laura Lazzeroni,Art B. Owen, published by which was released on 2000. Download full Plaid Models for Gene Expression Data Books now! Available in PDF, ePub and Kindle.

GET BOOK
Data Clustering
  • Author : Charu C. Aggarwal,Chandan K. Reddy
  • Publisher : CRC Press
  • Release Date : 2018-09-03

Research on the problem of clustering tends to be fragmented across the pattern recognition, database, data mining, and machine learning communities. Addressing this problem in a unified way, Data Clustering: Algorithms and Applications provides complete coverage of the entire area of clustering, from basic methods to more refined and complex

GET BOOK
Data Mining  Concepts and Techniques
  • Author : Jiawei Han,Jian Pei,Micheline Kamber
  • Publisher : Elsevier
  • Release Date : 2011-06-09

Data Mining: Concepts and Techniques provides the concepts and techniques in processing gathered data or information, which will be used in various applications. Specifically, it explains data mining and the tools used in discovering knowledge from the collected data. This book is referred as the knowledge discovery from data (KDD).

GET BOOK
Grouping Multidimensional Data
  • Author : Jacob Kogan,Charles Nicholas
  • Publisher : Taylor & Francis
  • Release Date : 2006-02-10

Publisher description

GET BOOK
Big Data Analytics  Systems  Algorithms  Applications
  • Author : C.S.R. Prabhu,Aneesh Sreevallabh Chivukula,Aditya Mogadala,Rohit Ghosh,L.M. Jenila Livingston
  • Publisher : Springer Nature
  • Release Date : 2019-10-14

This book provides a comprehensive survey of techniques, technologies and applications of Big Data and its analysis. The Big Data phenomenon is increasingly impacting all sectors of business and industry, producing an emerging new information ecosystem. On the applications front, the book offers detailed descriptions of various application areas for

GET BOOK
Neural Approaches to Dynamics of Signal Exchanges
  • Author : Anna Esposito,Marcos Faundez-Zanuy,Francesco Carlo Morabito,Eros Pasero
  • Publisher : Springer Nature
  • Release Date : 2019-09-18

The book presents research that contributes to the development of intelligent dialog systems to simplify diverse aspects of everyday life, such as medical diagnosis and entertainment. Covering major thematic areas: machine learning and artificial neural networks; algorithms and models; and social and biometric data for applications in human–computer interfaces,

GET BOOK
Understanding High Dimensional Spaces
  • Author : David B. Skillicorn
  • Publisher : Springer Science & Business Media
  • Release Date : 2012-09-24

High-dimensional spaces arise as a way of modelling datasets with many attributes. Such a dataset can be directly represented in a space spanned by its attributes, with each record represented as a point in the space with its position depending on its attribute values. Such spaces are not easy to

GET BOOK
Seamless R and C   Integration with Rcpp
  • Author : Dirk Eddelbuettel
  • Publisher : Springer Science & Business Media
  • Release Date : 2013-06-04

Rcpp is the glue that binds the power and versatility of R with the speed and efficiency of C++. With Rcpp, the transfer of data between R and C++ is nearly seamless, and high-performance statistical computing is finally accessible to most R users. Rcpp should be part of every statistician's

GET BOOK