Knowledge Discovery using Unsupervised Cognition

TL;DR

This paper introduces three techniques for knowledge discovery using an unsupervised learning algorithm called Unsupervised Cognition, focusing on pattern mining, feature selection, and dimensionality reduction to identify meaningful data features.

Contribution

It presents novel methods for knowledge discovery that improve over existing approaches by leveraging an Unsupervised Cognition model for pattern mining, feature selection, and dimensionality reduction.

Findings

Techniques outperform state-of-the-art in knowledge discovery

Effective identification of relevant features

Enhanced pattern extraction from datasets

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.