CONFIDENCE CIRCLE ON CORRESPONDENCE ANALYSIS USING SINGULAR VALUE DECOMPOSITION

Correspondence analysis is one of the method of multivariate data analysis to find special pattern in a data. This method is useful for visualizing qualitative data in a more attractively graphical presentation so it can be understood easily. Correspondence analysis algorithm will produce two or thr...

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Main Author: Dwi Kurniawati, Darmayanti
Format: Final Project
Language:Indonesia
Online Access:https://digilib.itb.ac.id/gdl/view/77281
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Institution: Institut Teknologi Bandung
Language: Indonesia
id id-itb.:77281
spelling id-itb.:772812023-08-25T14:22:40ZCONFIDENCE CIRCLE ON CORRESPONDENCE ANALYSIS USING SINGULAR VALUE DECOMPOSITION Dwi Kurniawati, Darmayanti Indonesia Final Project Correspondence analysis, data visualization, qualitative data analysis, multivariate analysis, singular value decomposition (SVD), confidence circle, dimension reduction, tracer study. INSTITUT TEKNOLOGI BANDUNG https://digilib.itb.ac.id/gdl/view/77281 Correspondence analysis is one of the method of multivariate data analysis to find special pattern in a data. This method is useful for visualizing qualitative data in a more attractively graphical presentation so it can be understood easily. Correspondence analysis algorithm will produce two or three euclidean subspaces, and will project all rows and columns profile in those euclidean subspaces. The dimension reduction technique used in this thesis for determining the euclidean subspaces is the Singular Value Decomposition (SVD). In this thesis, Correspondency Analysis will be used to discover the tendency of Graduates in choosing their jobs based on their GPA using tracer study data. In the last step, the correspondency analysis maps will also contain confidence circle. These confidence circles will show how close are several coordinates in different categories with each other. Also it will aids in highlighting some categories that doesn't have significant influence to the correspondency. text
institution Institut Teknologi Bandung
building Institut Teknologi Bandung Library
continent Asia
country Indonesia
Indonesia
content_provider Institut Teknologi Bandung
collection Digital ITB
language Indonesia
description Correspondence analysis is one of the method of multivariate data analysis to find special pattern in a data. This method is useful for visualizing qualitative data in a more attractively graphical presentation so it can be understood easily. Correspondence analysis algorithm will produce two or three euclidean subspaces, and will project all rows and columns profile in those euclidean subspaces. The dimension reduction technique used in this thesis for determining the euclidean subspaces is the Singular Value Decomposition (SVD). In this thesis, Correspondency Analysis will be used to discover the tendency of Graduates in choosing their jobs based on their GPA using tracer study data. In the last step, the correspondency analysis maps will also contain confidence circle. These confidence circles will show how close are several coordinates in different categories with each other. Also it will aids in highlighting some categories that doesn't have significant influence to the correspondency.
format Final Project
author Dwi Kurniawati, Darmayanti
spellingShingle Dwi Kurniawati, Darmayanti
CONFIDENCE CIRCLE ON CORRESPONDENCE ANALYSIS USING SINGULAR VALUE DECOMPOSITION
author_facet Dwi Kurniawati, Darmayanti
author_sort Dwi Kurniawati, Darmayanti
title CONFIDENCE CIRCLE ON CORRESPONDENCE ANALYSIS USING SINGULAR VALUE DECOMPOSITION
title_short CONFIDENCE CIRCLE ON CORRESPONDENCE ANALYSIS USING SINGULAR VALUE DECOMPOSITION
title_full CONFIDENCE CIRCLE ON CORRESPONDENCE ANALYSIS USING SINGULAR VALUE DECOMPOSITION
title_fullStr CONFIDENCE CIRCLE ON CORRESPONDENCE ANALYSIS USING SINGULAR VALUE DECOMPOSITION
title_full_unstemmed CONFIDENCE CIRCLE ON CORRESPONDENCE ANALYSIS USING SINGULAR VALUE DECOMPOSITION
title_sort confidence circle on correspondence analysis using singular value decomposition
url https://digilib.itb.ac.id/gdl/view/77281
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