Correspondence Analysis and Data Coding with Java and R
Regular price
€80.99
14 days return policy
Shipping & Delivery
?2 Distance
A01=Fionn Murtagh
advanced correspondence analysis case studies
artificial intelligence analytics
Author_Fionn Murtagh
Burt Table
categorical data interpretation
Category=PBT
Category=PS
Category=UMX
clustering
CO2 Ctr
Code Category
component
Conditional Possibility
contingency
Correspondence Analysis
data visualization techniques
distance
Dream Reports
eigenvalue
eq_bestseller
eq_computing
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
eq_non-fiction
eq_science
Full Words
Fuzzy Coding
Geometric Brownian Motion
hierarchical
Hierarchical Clustering
hierarchical clustering methods
Input Data Coding
J1 J2 J3 J4 J5
Minimum Variance Method
Multiple Correspondence Analysis
multivariate
multivariate data analysis
principal
Principal Components Analysis
rate
S1 dH
s1dL
St Molar
Supplementary Elements
Supplementary Rows
table
text mining applications
Thai Dogs
Tool Words
Word Forms
WTS
Χ2 Distance
Product details
- ISBN 9780367392734
- Weight: 362g
- Dimensions: 156 x 234mm
- Publication Date: 05 Sep 2019
- Publisher: Taylor & Francis Ltd
- Publication City/Country: GB
- Product Form: Paperback
Secure
checkout
Fast Shipping
Easy returns
Developed by Jean-Paul Benzérci more than 30 years ago, correspondence analysis as a framework for analyzing data quickly found widespread popularity in Europe. The topicality and importance of correspondence analysis continue, and with the tremendous computing power now available and new fields of application emerging, its significance is greater than ever.
Correspondence Analysis and Data Coding with Java and R clearly demonstrates why this technique remains important and in the eyes of many, unsurpassed as an analysis framework. After presenting some historical background, the author presents a theoretical overview of the mathematics and underlying algorithms of correspondence analysis and hierarchical clustering. The focus then shifts to data coding, with a survey of the widely varied possibilities correspondence analysis offers and introduction of the Java software for correspondence analysis, clustering, and interpretation tools. A chapter of case studies follows, wherein the author explores applications to areas such as shape analysis and time-evolving data. The final chapter reviews the wealth of studies on textual content as well as textual form, carried out by Benzécri and his research lab. These discussions show the importance of correspondence analysis to artificial intelligence as well as to stylometry and other fields.
This book not only shows why correspondence analysis is important, but with a clear presentation replete with advice and guidance, also shows how to put this technique into practice. Downloadable software and data sets allow quick, hands-on exploration of innovative correspondence analysis applications.
Murtagh, Fionn
