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 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.

@article{Tin2024,
  title     = {Visualization of Personality and Phobia Type Clustering with GMM and Spectral},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {9},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {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},
  doi       = {10.14569/IJACSA.2024.0150988},
  url       = {https://doi.org/10.14569/IJACSA.2024.0150988}
}