Clustering Data Streams Using Mass Estimation

2013 ◽  
Author(s):  
Andrei Sorin Sabau
Keyword(s):  
Data Streams ◽  
Mass Estimation ◽  
Clustering Data

Requirements for clustering data streams

2002 ◽  
Vol 3 (2) ◽  
pp. 23-27 ◽  
Author(s):  
Daniel Barbará
Keyword(s):  
Data Streams ◽  
Clustering Data

An evolutionary algorithm for clustering data streams with a variable number of clusters

2017 ◽  
Vol 67 ◽  
pp. 228-238 ◽  
Author(s):  
Jonathan de Andrade Silva ◽  
Eduardo Raul Hruschka ◽  
João Gama
Keyword(s):  
Evolutionary Algorithm ◽  
Data Streams ◽  
Variable Number ◽  
Number Of Clusters ◽  
Clustering Data

Clustering data streams: theory and practice

2003 ◽  
Vol 15 (3) ◽  
pp. 515-528 ◽  
Author(s):  
S. Guha ◽  
A. Meyerson ◽  
N. Mishra ◽  
R. Motwani ◽  
L. O'Callaghan
Keyword(s):  
Data Streams ◽  
Theory And Practice ◽  
Clustering Data

scholarly journals Incremental autoencoders for text streams clustering in social networks

2021 ◽  
Vol 27 (11) ◽  
pp. 1203-1221
Author(s):  
Amal Rekik ◽  
Salma Jamoussi
Keyword(s):  
Social Networks ◽  
Big Data ◽  
Deep Learning ◽  
Data Streams ◽  
High Speed ◽  
Clustering Method ◽  
Text Streams ◽  
Data Field ◽  
Learning Technique ◽  
Clustering Data

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.

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