The Impact of Clustering for Learning Semantic Categories

Mário Antunes, Diogo Gomes, Rui L. Aguiar

Abstract

The evergrowing number of small devices with sensing capabilities produce massive amounts of diverse data. However, it becomes increasingly difficult to manage all these new data sources. Currently there is no single way to represent, share, and understand IoT data, leading to information silos that hinder the realization of complex IoT/M2M scenarios. IoT/M2M scenarios can only achieve their full potential when the devices become intelligent: work and learn together with minimal human intervention. We developed methods to estimate semantic similarity based on distributional profiles, cluster algorithm were used to learn semantic categories and improve the model accuracy. In this paper we discuss the impact of the cluster algorithm and respective heuristic to estimate inital parameters in the task of learning semantic categories. Our evaluation has shown that k-means combined with silhouette method achieved the higher result.

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Paper Citation


in Harvard Style

Antunes M., Gomes D. and Aguiar R. (2018). The Impact of Clustering for Learning Semantic Categories.In Proceedings of the 3rd International Conference on Internet of Things, Big Data and Security - Volume 1: IoTBDS, ISBN 978-989-758-296-7, pages 320-327. DOI: 10.5220/0006813603200327


in Bibtex Style

@conference{iotbds18,
author={Mário Antunes and Diogo Gomes and Rui L. Aguiar},
title={The Impact of Clustering for Learning Semantic Categories},
booktitle={Proceedings of the 3rd International Conference on Internet of Things, Big Data and Security - Volume 1: IoTBDS,},
year={2018},
pages={320-327},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006813603200327},
isbn={978-989-758-296-7},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 3rd International Conference on Internet of Things, Big Data and Security - Volume 1: IoTBDS,
TI - The Impact of Clustering for Learning Semantic Categories
SN - 978-989-758-296-7
AU - Antunes M.
AU - Gomes D.
AU - Aguiar R.
PY - 2018
SP - 320
EP - 327
DO - 10.5220/0006813603200327