Multiple Correspondence Analysis and Geometric Data Analysis

2021 ◽  
pp. 177-184
Author(s):  
Amal Tawfik ◽  
Stephan Davidshofer
Keyword(s):  
Data Analysis ◽  
Correspondence Analysis ◽  
Multiple Correspondence Analysis ◽  
Principal Component ◽  
Categorical Variables ◽  
Large Set ◽  
Geometric Data ◽  
Jean Paul ◽  
Geometric Data Analysis ◽  
Or Groups

This chapter focuses on multiple correspondence analysis (MCA), which is a factor analysis statistical method used to analyse relations between a large set of categorical variables. Developed by Jean-Paul Benzécri in the early 1970s, MCA is one of the principal methods of geometric data analysis (GDA). Three different statistical methods can be identified as GDA: correspondence analysis (CA), which enables the cross-tabulation of two categorical variables; MCA for the analysis of a matrix of individuals and categorical variables; and principal component analysis (PCA), which uses numerical variables. In GDA, data is represented as a cloud of points to allow statistical interpretations. Although MCA is a relational method, it differs from social network analysis (SNA) as it focuses on the objective relations that characterize actors or groups, rather than the effective relations.

Performance Improvement in the Pattern Classification of Nominal Data Sets Applying Multiple Correspondence Analysis

2014 ◽  
Vol 670-671 ◽  
pp. 1482-1487
Author(s):  
Rodrigo Clemente Thom de Souza ◽  
Maria Teresinha Arns Steiner ◽  
Leandro dos Santos Coelho
Keyword(s):  
Correspondence Analysis ◽  
Multiple Correspondence Analysis ◽  
Principal Component ◽  
Knowledge Discovery In Databases ◽  
Data Sets ◽  
Data Types ◽  
Nominal Data ◽  
Linear Discriminant ◽  
Whole Process ◽  
Geometric Data Analysis

Classification is a supervised learning problem used to discriminate data instances in different classes. The solution to this problem is obtained through algorithms (classifiers) that look for patterns of relationships between classes in known cases, using these relationships to classify unknown cases. The performance of the classifiers depends substantially of the data types. In order to give proper treatment to nominal data, this paper shows that the application of previous transformations can substantially improve the performance of classifiers, bringing significant benefits to the result of the whole process of Knowledge Discovery in Databases (KDD). This paper uses three different data sets with nominal data and two well-known classifiers: the Linear Discriminant Analysis (LDA), and the Naïve-Bayes (NB). For data transformation, the paper applies an approach called Geometric Data Analysis (GDA). The GDA techniques compared in this paper are the traditional Principal Component Analysis (PCA) and the underexplored Multiple Correspondence Analysis (MCA). The results confirm the capability of the GDA transformation to improve the classification accuracy and attest the superiority of the MCA in comparison with its precursor, the PCA, when applied to nominal data.

scholarly journals Using multiple correspondence analysis to identify behaviour patterns associated with overweight and obesity in Vanuatu adults

2019 ◽  
Vol 22 (09) ◽  
pp. 1533-1544 ◽  
Author(s):  
Andrew van Horn ◽  
Charles A Weitz ◽  
Kathryn M Olszowy ◽  
Kelsey N Dancause ◽  
Cheng Sun ◽  
...  
Keyword(s):  
Body Fat ◽  
Correspondence Analysis ◽  
Categorical Data ◽  
Multiple Correspondence Analysis ◽  
Overweight And Obesity ◽  
Data Sets ◽  
Pacific Island ◽  
Binary Responses ◽  
Or Groups ◽  
The Us

AbstractObjectiveThe present study evaluates the use of multiple correspondence analysis (MCA), a type of exploratory factor analysis designed to reduce the dimensionality of large categorical data sets, in identifying behaviours associated with measures of overweight/obesity in Vanuatu, a rapidly modernizing Pacific Island country.DesignStarting with seventy-three true/false questions regarding a variety of behaviours, MCA identified twelve most significantly associated with modernization status and transformed the aggregate binary responses of participants to these twelve questions into a linear scale. Using this scale, individuals were separated into three modernization groups (tertiles) among which measures of body fat were compared and OR for overweight/obesity were computed.SettingVanuatu.ParticipantsNi-Vanuatu adults (n 810) aged 20–85 years.ResultsAmong individuals in the tertile characterized by positive responses to most of or all the twelve modernization questions, weight and measures of body fat and the likelihood that measures of body fat were above the US 75th percentile were significantly greater compared with individuals in the tertiles characterized by mostly or partly negative responses.ConclusionsThe study indicates that MCA can be used to identify individuals or groups at risk for overweight/obesity, based on answers to simply-put questions. MCA therefore may be useful in areas where obtaining detailed information about modernization status is constrained by time, money or manpower.

Combinatorial Inference in Geometric Data Analysis

2019 ◽  
Author(s):  
Brigitte Le Roux ◽  
Solène Bienaise ◽  
Jean-Luc Durand
Keyword(s):  
Data Analysis ◽  
Geometric Data ◽  
Geometric Data Analysis
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