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.

翻译：知识发现是理解和解释数据集以及发现其组成部分之间潜在关系的关键。无监督认知是一种新颖的无监督学习算法，专注于对已学习数据进行建模。本文提出了三种在已训练的无监督认知模型上执行知识发现的技术。具体而言，我们提出了一种模式挖掘技术、一种基于前述模式挖掘技术的特征选择技术，以及一种基于前述特征选择技术的降维技术。最终目标是区分相关特征与无关特征，并利用它们构建可从中提取有意义模式的模型。我们通过实证实验评估了所提出的方法，发现其在知识发现方面超越了现有技术水平。