Applied Biclustering Methods for Big and High-Dimensional Data Using R

by Kasim, Adetayo

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Title Statements

Full title :Applied Biclustering Methods for Big and High-Dimensional Data Using R

Edition Statements

Publication Date:©2016
Edition:1st ed

Publication Statements

Publisher(s):Chapman and Hall/CRC
Place of Publication:Boca Raton
Language:eng
Publication Date:©2016

Notes

19.4.1. Data Structure and Notations.

The Physical Object

Description:1 online resource (428 pages)

Edition Identifiers

ISBN:9781482208245, 1482208245, 9781315356396, 1315356392, 9781315337333, 1315337339, 9781315373966, 1315373963
oclc:on1076650801
LC Classification:QA76.9.B45.A67 2017
Dewey Decimal:570.28557

Cover -- Title Page -- Copyright Page -- Contents -- Preface -- Contributors -- R Packages and Products -- 1. Introduction -- 1.1. From Clustering to Biclustering -- 1.2. We R a Community -- 1.3. Biclustering for Cloud Computing -- 1.4. Book Structure -- 1.5. Datasets -- 1.5.1. Dutch Breast Cancer Data -- 1.5.2. Diffuse Large B-Cell Lymphoma (DLBCL) -- 1.5.3. Multiple Tissue Types Data -- 1.5.4. CMap Dataset -- 1.5.5. NCI60 Panel -- 1.5.6. 1000 Genomes Project -- 1.5.7. Tourism Survey Data -- 1.5.8. Toxicogenomics Project -- 1.5.9. Yeast Data -- 1.5.10. mglu2 Project -- 1.5.11. TCGA Data -- 1.5.12. NBA Data -- 1.5.13. Colon Cancer Data -- 2. From Cluster Analysis to Biclustering -- 2.1. Cluster Analysis -- 2.1.1. An Introduction -- 2.1.2. Dissimilarity Measures and Similarity Measures -- 2.1.2.1. Example 1: Clustering Compounds in the CMAP DataBased on Chemical Similarity -- 2.1.2.2. Example 2 -- 2.1.3. Hierarchical Clustering -- 2.1.3.1. Example 1 -- 2.1.3.2. Example 2 -- 2.1.4. ABC Dissimilarity for High-Dimensional Data -- 2.2. Biclustering: A Graphical Tour -- 2.2.1. Global versus Local Patterns -- 2.2.2. Bicluster's Type -- 2.2.3. Bicluster's Configuration -- Part I: Bicluste…