Staff View: Visualization of personality and phobia type clustering with GMM and spectral

Visualization of personality and phobia type clustering with GMM and spectral

Personality traits, the unique characteristics defining individuals, have intrigued philosophers and scholars for centuries. With recent advances in machine learning, there is an opportunity to revolutionize how we understand and differentiate personality traits. This study seeks to develop a robust...

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Main Authors:	Ting Tin Tin, Cheok Jia Wei, Ong Tzi Min, Lim Siew Mooi, Lee Kuok Tiung, Ali Aitizaz, Chaw Jun Kit, Ayodeji Olalekan Salau
Format:	Article
Language:	English
Published:	The Science and Information (SAI) Organization Limited 2024
Subjects:	BF511-593 Affection. Feeling. Emotion BF698-698.9 Personality
Online Access:	https://eprints.ums.edu.my/id/eprint/42997/1/FULL%20TEXT.pdf https://eprints.ums.edu.my/id/eprint/42997/ https://dx.doi.org/10.14569/IJACSA.2024.0150988
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id	my.ums.eprints.42997
record_format	eprints
spelling	my.ums.eprints.429972025-03-03T23:57:57Z https://eprints.ums.edu.my/id/eprint/42997/ Visualization of personality and phobia type clustering with GMM and spectral Ting Tin Tin Cheok Jia Wei Ong Tzi Min Lim Siew Mooi Lee Kuok Tiung Ali Aitizaz Chaw Jun Kit Ayodeji Olalekan Salau BF511-593 Affection. Feeling. Emotion BF698-698.9 Personality Personality traits, the unique characteristics defining individuals, have intrigued philosophers and scholars for centuries. With recent advances in machine learning, there is an opportunity to revolutionize how we understand and differentiate personality traits. This study seeks to develop a robust cluster analysis approach (unsupervised learning) to efficiently and accurately classify individuals based on their personality traits, overcoming the limitations of manual classification. The problem at hand is to create a system that can handle the subjective nature of qualitative personality data, providing insights into how people interact, collaborate, and behave in various social contexts and thus increase learning opportunities. To achieve this, various unsupervised clustering techniques, including spectral clustering and Gaussian mixture models, will be employed to identify similarities in unlabeled data collected through interview questions. The clustering approach is crucial in helping policy makers to identify suitable approaches to improve teamwork efficiency in both educational institutions and job industries. The Science and Information (SAI) Organization Limited 2024 Article NonPeerReviewed text en https://eprints.ums.edu.my/id/eprint/42997/1/FULL%20TEXT.pdf Ting Tin Tin and Cheok Jia Wei and Ong Tzi Min and Lim Siew Mooi and Lee Kuok Tiung and Ali Aitizaz and Chaw Jun Kit and Ayodeji Olalekan Salau (2024) Visualization of personality and phobia type clustering with GMM and spectral. International Journal of Advanced Computer Science and Applications (IJACSA), 15 (9). pp. 1-10. ISSN 2156-5570 https://dx.doi.org/10.14569/IJACSA.2024.0150988
institution	Universiti Malaysia Sabah
building	UMS Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Malaysia Sabah
content_source	UMS Institutional Repository
url_provider	http://eprints.ums.edu.my/
language	English
topic	BF511-593 Affection. Feeling. Emotion BF698-698.9 Personality
spellingShingle	BF511-593 Affection. Feeling. Emotion BF698-698.9 Personality Ting Tin Tin Cheok Jia Wei Ong Tzi Min Lim Siew Mooi Lee Kuok Tiung Ali Aitizaz Chaw Jun Kit Ayodeji Olalekan Salau Visualization of personality and phobia type clustering with GMM and spectral
description	Personality traits, the unique characteristics defining individuals, have intrigued philosophers and scholars for centuries. With recent advances in machine learning, there is an opportunity to revolutionize how we understand and differentiate personality traits. This study seeks to develop a robust cluster analysis approach (unsupervised learning) to efficiently and accurately classify individuals based on their personality traits, overcoming the limitations of manual classification. The problem at hand is to create a system that can handle the subjective nature of qualitative personality data, providing insights into how people interact, collaborate, and behave in various social contexts and thus increase learning opportunities. To achieve this, various unsupervised clustering techniques, including spectral clustering and Gaussian mixture models, will be employed to identify similarities in unlabeled data collected through interview questions. The clustering approach is crucial in helping policy makers to identify suitable approaches to improve teamwork efficiency in both educational institutions and job industries.
format	Article
author	Ting Tin Tin Cheok Jia Wei Ong Tzi Min Lim Siew Mooi Lee Kuok Tiung Ali Aitizaz Chaw Jun Kit Ayodeji Olalekan Salau
author_facet	Ting Tin Tin Cheok Jia Wei Ong Tzi Min Lim Siew Mooi Lee Kuok Tiung Ali Aitizaz Chaw Jun Kit Ayodeji Olalekan Salau
author_sort	Ting Tin Tin
title	Visualization of personality and phobia type clustering with GMM and spectral
title_short	Visualization of personality and phobia type clustering with GMM and spectral
title_full	Visualization of personality and phobia type clustering with GMM and spectral
title_fullStr	Visualization of personality and phobia type clustering with GMM and spectral
title_full_unstemmed	Visualization of personality and phobia type clustering with GMM and spectral
title_sort	visualization of personality and phobia type clustering with gmm and spectral
publisher	The Science and Information (SAI) Organization Limited
publishDate	2024
url	https://eprints.ums.edu.my/id/eprint/42997/1/FULL%20TEXT.pdf https://eprints.ums.edu.my/id/eprint/42997/ https://dx.doi.org/10.14569/IJACSA.2024.0150988
_version_	1825809972493025280
score	13.244413

Visualization of personality and phobia type clustering with GMM and spectral

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