Characterizing N-Dimension Data Clusters: A Density-based Metric for Compactness and Homogeneity Evaluation

Dylan Molinié, Kurosh Madani

2021

Abstract

The new challenges Science is facing nowadays are legion; they mostly focus on high level technology, and more specifically Robotics, Internet of Things, Smart Automation (cities, houses, plants, buildings, etc.), and more recently Cyber-Physical Systems and Industry 4.0. For a long time, cognitive systems have been seen as a mere dream only worth of Science Fiction. Even though there is much to be done, the researches and progress made in Artificial Intelligence have let cognition-based systems make a great leap forward, which is now an actual great area of interest for many scientists and industrialists. Nonetheless, there are two main obstacles to system’s smartness: computational limitations and the infinite number of states to define; Machine Learning-based algorithms are perfectly suitable to Cognition and Automation, for they allow an automatic – and accurate – identification of the systems, usable as knowledge for later regulation. In this paper, we discuss the benefits of Machine Learning, and we present some new avenues of reflection for automatic behavior correctness identification through space partitioning, and density conceptualization and computation.

Paper Citation

in Harvard Style

Molinié D. and Madani K. (2021). Characterizing N-Dimension Data Clusters: A Density-based Metric for Compactness and Homogeneity Evaluation. In Proceedings of the 2nd International Conference on Innovative Intelligent Industrial Production and Logistics - Volume 1: IN4PL, ISBN 978-989-758-535-7, pages 13-24. DOI: 10.5220/0010657500003062

in Bibtex Style

@conference{in4pl21,
author={Dylan Molinié and Kurosh Madani},
title={Characterizing N-Dimension Data Clusters: A Density-based Metric for Compactness and Homogeneity Evaluation},
booktitle={Proceedings of the 2nd International Conference on Innovative Intelligent Industrial Production and Logistics - Volume 1: IN4PL,},
year={2021},
pages={13-24},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010657500003062},
isbn={978-989-758-535-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 2nd International Conference on Innovative Intelligent Industrial Production and Logistics - Volume 1: IN4PL,
TI - Characterizing N-Dimension Data Clusters: A Density-based Metric for Compactness and Homogeneity Evaluation
SN - 978-989-758-535-7
AU - Molinié D.
AU - Madani K.
PY - 2021
SP - 13
EP - 24
DO - 10.5220/0010657500003062