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Big Data Clustering via Community Detection and Hyperbolic Network Embedding in IoT Applications
In this paper, we present a novel data clustering framework for big sensory data produced by IoT applications. Based on a network representation of the relations among multi-dimensional data, data clustering is mapped to node clustering over the produced data graphs. To address the potential very la...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5948775/ https://www.ncbi.nlm.nih.gov/pubmed/29662043 http://dx.doi.org/10.3390/s18041205 |
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author | Karyotis, Vasileios Tsitseklis, Konstantinos Sotiropoulos, Konstantinos Papavassiliou, Symeon |
author_facet | Karyotis, Vasileios Tsitseklis, Konstantinos Sotiropoulos, Konstantinos Papavassiliou, Symeon |
author_sort | Karyotis, Vasileios |
collection | PubMed |
description | In this paper, we present a novel data clustering framework for big sensory data produced by IoT applications. Based on a network representation of the relations among multi-dimensional data, data clustering is mapped to node clustering over the produced data graphs. To address the potential very large scale of such datasets/graphs that test the limits of state-of-the-art approaches, we map the problem of data clustering to a community detection one over the corresponding data graphs. Specifically, we propose a novel computational approach for enhancing the traditional Girvan–Newman (GN) community detection algorithm via hyperbolic network embedding. The data dependency graph is embedded in the hyperbolic space via Rigel embedding, allowing more efficient computation of edge-betweenness centrality needed in the GN algorithm. This allows for more efficient clustering of the nodes of the data graph in terms of modularity, without sacrificing considerable accuracy. In order to study the operation of our approach with respect to enhancing GN community detection, we employ various representative types of artificial complex networks, such as scale-free, small-world and random geometric topologies, and frequently-employed benchmark datasets for demonstrating its efficacy in terms of data clustering via community detection. Furthermore, we provide a proof-of-concept evaluation by applying the proposed framework over multi-dimensional datasets obtained from an operational smart-city/building IoT infrastructure provided by the Federated Interoperable Semantic IoT/cloud Testbeds and Applications (FIESTA-IoT) testbed federation. It is shown that the proposed framework can be indeed used for community detection/data clustering and exploited in various other IoT applications, such as performing more energy-efficient smart-city/building sensing. |
format | Online Article Text |
id | pubmed-5948775 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-59487752018-05-17 Big Data Clustering via Community Detection and Hyperbolic Network Embedding in IoT Applications Karyotis, Vasileios Tsitseklis, Konstantinos Sotiropoulos, Konstantinos Papavassiliou, Symeon Sensors (Basel) Article In this paper, we present a novel data clustering framework for big sensory data produced by IoT applications. Based on a network representation of the relations among multi-dimensional data, data clustering is mapped to node clustering over the produced data graphs. To address the potential very large scale of such datasets/graphs that test the limits of state-of-the-art approaches, we map the problem of data clustering to a community detection one over the corresponding data graphs. Specifically, we propose a novel computational approach for enhancing the traditional Girvan–Newman (GN) community detection algorithm via hyperbolic network embedding. The data dependency graph is embedded in the hyperbolic space via Rigel embedding, allowing more efficient computation of edge-betweenness centrality needed in the GN algorithm. This allows for more efficient clustering of the nodes of the data graph in terms of modularity, without sacrificing considerable accuracy. In order to study the operation of our approach with respect to enhancing GN community detection, we employ various representative types of artificial complex networks, such as scale-free, small-world and random geometric topologies, and frequently-employed benchmark datasets for demonstrating its efficacy in terms of data clustering via community detection. Furthermore, we provide a proof-of-concept evaluation by applying the proposed framework over multi-dimensional datasets obtained from an operational smart-city/building IoT infrastructure provided by the Federated Interoperable Semantic IoT/cloud Testbeds and Applications (FIESTA-IoT) testbed federation. It is shown that the proposed framework can be indeed used for community detection/data clustering and exploited in various other IoT applications, such as performing more energy-efficient smart-city/building sensing. MDPI 2018-04-15 /pmc/articles/PMC5948775/ /pubmed/29662043 http://dx.doi.org/10.3390/s18041205 Text en © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Karyotis, Vasileios Tsitseklis, Konstantinos Sotiropoulos, Konstantinos Papavassiliou, Symeon Big Data Clustering via Community Detection and Hyperbolic Network Embedding in IoT Applications |
title | Big Data Clustering via Community Detection and Hyperbolic Network Embedding in IoT Applications |
title_full | Big Data Clustering via Community Detection and Hyperbolic Network Embedding in IoT Applications |
title_fullStr | Big Data Clustering via Community Detection and Hyperbolic Network Embedding in IoT Applications |
title_full_unstemmed | Big Data Clustering via Community Detection and Hyperbolic Network Embedding in IoT Applications |
title_short | Big Data Clustering via Community Detection and Hyperbolic Network Embedding in IoT Applications |
title_sort | big data clustering via community detection and hyperbolic network embedding in iot applications |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5948775/ https://www.ncbi.nlm.nih.gov/pubmed/29662043 http://dx.doi.org/10.3390/s18041205 |
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