Knowledge Discovery using Unsupervised Cognition
<|reference_start|>Knowledge Discovery using Unsupervised Cognition: Knowledge discovery is key to understand and interpret a dataset, as well as to find the underlying relationships between its components. Unsupervised Cognition is a novel unsupervised learning algorithm that focus on modelling the learned data. This paper presents three techniques to perform knowledge discovery over an already trained Unsupervised Cognition model. Specifically, we present a technique for pattern mining, a technique for feature selection based on the previous pattern mining technique, and a technique for dimensionality reduction based on the previous feature selection technique. The final goal is to distinguish between relevant and irrelevant features and use them to build a model from which to extract meaningful patterns. We evaluated our proposals with empirical experiments and found that they overcome the state-of-the-art in knowledge discovery.<|reference_end|>
@article{ibias2024knowledge,
    title={Knowledge Discovery using Unsupervised Cognition},
    author={Alfredo Ibias, Hector Antona, Guillem Ramirez-Miranda, Enric Guinovart},
    journal={arXiv preprint arXiv:2409.20064},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.20064},
    primaryClass={cs.LG cs.AI}
}