🤖 AI Summary
To address challenges in unsupervised knowledge discovery—including weak modeling of feature correlations, poor pattern interpretability, and semantic distortion in dimensionality reduction—this paper proposes a three-stage analytical framework grounded in an Unsupervised Cognition model: (1) association pattern mining, (2) cognition-significance-driven interpretable feature selection, and (3) semantic-consistency-constrained dimensionality reduction. It pioneers the end-to-end integration of cognitive modeling with knowledge discovery, enabling fully label-free, interpretable analysis. Evaluated on diverse multi-source empirical datasets, the method consistently outperforms state-of-the-art approaches across all core metrics: pattern completeness (+12.7%), feature discriminability (+9.4%), and dimensionality-reduction interpretability (+15.3%).
📝 Abstract
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.