Knowledge Discovery using Unsupervised Cognition
This work addresses the problem of extracting meaningful knowledge and patterns from datasets for data scientists and researchers, offering an incremental improvement over existing knowledge discovery methods.
This paper introduces three techniques for knowledge discovery—pattern mining, feature selection, and dimensionality reduction—that operate on an already trained Unsupervised Cognition model. These techniques aim to identify relevant features and extract meaningful patterns from data, outperforming state-of-the-art methods in empirical evaluations.
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.