A Survey of Clustering Algorithms for Big Data: Taxonomy and Empirical Analysis

Clustering algorithms have emerged as an alternative powerful meta-learning tool to accurately analyze the massive volume of data generated by modern applications. In particular, their main goal is to categorize data into clusters such that objects are grouped in the same cluster when they are simil...

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Veröffentlicht in:	IEEE transactions on emerging topics in computing Jg. 2; H. 3; S. 267 - 279
Hauptverfasser:	Fahad, Adil, Alshatri, Najlaa, Tari, Zahir, Alamri, Abdullah, Khalil, Ibrahim, Zomaya, Albert Y., Foufou, Sebti, Bouras, Abdelaziz
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	IEEE 01.09.2014 Institute of Electrical and Electronics Engineers
Schlagworte:	Algorithm design and analysis Big data Clustering algorithms Clustering methods Computer Science Neural networks Partitioning algorithms Taxonomies big data Clustering algorithms unsupervised learning
ISSN:	2168-6750, 2168-6750
Online-Zugang:	Volltext
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