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Visualization and Verbalization of Data (Chapman & Hall/CRC Computer Science & Data Analysis)

By: Jorg Blasius (Edited by) , Michael Greenacre (Edited by)

Manufacture on Demand

Ksh 36,750.00

Format: Hardback or Cased Book

ISBN-10: 1466589809

ISBN-13: 9781466589803

Collection / Series: Chapman & Hall/CRC Computer Science & Data Analysis

Collection Type: Publisher collection

Publisher: Taylor & Francis Inc

Imprint: CRC Press Inc

Country of Manufacture: US

Country of Publication: GB

Publication Date: Apr 10th, 2014

Publication Status: Active

Product extent: 392 Pages

Weight: 730.00 grams

Dimensions (height x width x thickness): 24.20 x 16.10 x 2.70 cms

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This volume shows how correspondence analysis and related techniques enable the display of data in graphical form, which results in the verbalization of the structures in data. Renowned researchers in the field trace the history of these techniques and cover their current applications. Examples include the spatial visualization of multivariate data, cluster analysis in computer science, the transformation of a textual data set into numerical data, the use of quantitative and qualitative variables in multiple factor analysis, and more.

Visualization and Verbalization of Data shows how correspondence analysis and related techniques enable the display of data in graphical form, which results in the verbalization of the structures in data. Renowned researchers in the field trace the history of these techniques and cover their current applications.

The first part of the book explains the historical origins of correspondence analysis and associated methods. The second part concentrates on the contributions made by the school of Jean-Paul Benzécri and related movements, such as social space and geometric data analysis. Although these topics are viewed from a French perspective, the book makes them understandable to an international audience.

Throughout the text, well-known experts illustrate the use of the methods in practice. Examples include the spatial visualization of multivariate data, cluster analysis in computer science, the transformation of a textual data set into numerical data, the use of quantitative and qualitative variables in multiple factor analysis, different possibilities of recoding data prior to visualization, and the application of duality diagram theory to the analysis of a contingency table.


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