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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Bibliographic Details
Published in:International journal of advanced computer science & applications 2024-01, Vol.15 (9)
Main Authors: Tin, Ting Tin, Wei, Cheok Jia, Min, Ong Tzi, Mooi, Lim Siew, Tiung, Lee Kuok, Aitizaz, Ali, Kit, Chaw Jun, Salau, Ayodeji Olalekan
Format: Article
Language:English
Subjects:
Algorithms
Behavior
Cluster analysis
Clustering
Collaboration
Computer engineering
Computer science
Efficiency
Eigenvectors
Fear & phobias
Information technology
Machine learning
Personality
Personality traits
Philosophers
Probabilistic models
Qualitative analysis
Research methodology
Unsupervised learning
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Summary: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.
ISSN:2158-107X
2156-5570
DOI:10.14569/IJACSA.2024.0150988