Incremental autoencoders for text streams clustering in social networks

Clustering data streams in order to detect trending topic on social networks is a chal- lenging task that interests the researchers in the big data field. In fact, analyzing such data needs several requirements to be addressed due to their large amount and evolving nature. For this purpose, we propo...

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Autores principales:	Amal Rekik, Salma Jamoussi
Formato:	article
Lenguaje:	EN
Publicado:	Graz University of Technology 2021
Materias:	Social network Topic extraction Data streams Cl Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/869cc13eadc74773871e48e90135a230
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id	oai:doaj.org-article:869cc13eadc74773871e48e90135a230
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spelling	oai:doaj.org-article:869cc13eadc74773871e48e90135a2302021-11-30T04:30:11ZIncremental autoencoders for text streams clustering in social networks10.3897/jucs.767700948-6968https://doaj.org/article/869cc13eadc74773871e48e90135a2302021-11-01T00:00:00Zhttps://lib.jucs.org/article/76770/download/pdf/https://lib.jucs.org/article/76770/download/xml/https://lib.jucs.org/article/76770/https://doaj.org/toc/0948-6968Clustering data streams in order to detect trending topic on social networks is a chal- lenging task that interests the researchers in the big data field. In fact, analyzing such data needs several requirements to be addressed due to their large amount and evolving nature. For this purpose, we propose, in this paper, a new evolving clustering method which can take into account the incremental nature of the data and meet with its principal requirements. Our method explores a deep learning technique to learn incrementally from unlabelled examples generated at high speed which need to be clustered instantly. To evaluate the performance of our method, we have conducted several experiments using the Sanders, HCR and Terr-Attacks datasets.Amal RekikSalma JamoussiGraz University of TechnologyarticleSocial networkTopic extractionData streamsClElectronic computers. Computer scienceQA75.5-76.95ENJournal of Universal Computer Science, Vol 27, Iss 11, Pp 1203-1221 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Social network Topic extraction Data streams Cl Electronic computers. Computer science QA75.5-76.95
spellingShingle	Social network Topic extraction Data streams Cl Electronic computers. Computer science QA75.5-76.95 Amal Rekik Salma Jamoussi Incremental autoencoders for text streams clustering in social networks
description	Clustering data streams in order to detect trending topic on social networks is a chal- lenging task that interests the researchers in the big data field. In fact, analyzing such data needs several requirements to be addressed due to their large amount and evolving nature. For this purpose, we propose, in this paper, a new evolving clustering method which can take into account the incremental nature of the data and meet with its principal requirements. Our method explores a deep learning technique to learn incrementally from unlabelled examples generated at high speed which need to be clustered instantly. To evaluate the performance of our method, we have conducted several experiments using the Sanders, HCR and Terr-Attacks datasets.
format	article
author	Amal Rekik Salma Jamoussi
author_facet	Amal Rekik Salma Jamoussi
author_sort	Amal Rekik
title	Incremental autoencoders for text streams clustering in social networks
title_short	Incremental autoencoders for text streams clustering in social networks
title_full	Incremental autoencoders for text streams clustering in social networks
title_fullStr	Incremental autoencoders for text streams clustering in social networks
title_full_unstemmed	Incremental autoencoders for text streams clustering in social networks
title_sort	incremental autoencoders for text streams clustering in social networks
publisher	Graz University of Technology
publishDate	2021
url	https://doaj.org/article/869cc13eadc74773871e48e90135a230
work_keys_str_mv	AT amalrekik incrementalautoencodersfortextstreamsclusteringinsocialnetworks AT salmajamoussi incrementalautoencodersfortextstreamsclusteringinsocialnetworks
_version_	1718406760201453568

Incremental autoencoders for text streams clustering in social networks

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