Visualization of personality and phobia type clustering with GMM and spectral

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

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Abstract

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.

Item Type:	Article
Keyword:	Unsupervised learning; learning opportunities; clustering; personality; machine learning; Gaussian mixture model; spectral clustering
Subjects:	B Philosophy. Psychology. Religion > BF Psychology > BF1-990 Psychology > BF511-593 Affection. Feeling. Emotion B Philosophy. Psychology. Religion > BF Psychology > BF1-990 Psychology > BF698-698.9 Personality
Department:	FACULTY > Faculty of Social Sciences and Humanities
Depositing User:	ABDULLAH BIN SABUDIN -
Date Deposited:	04 Mar 2025 07:57
Last Modified:	04 Mar 2025 07:57
URI:	https://eprints.ums.edu.my/id/eprint/42997

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