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 (The Science and Information (SAI) Organization Limited, 2024)
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.