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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Vydáno v:IEEE transactions on emerging topics in computing Ročník 2; číslo 3; s. 267 - 279
Hlavní autoři: Fahad, Adil, Alshatri, Najlaa, Tari, Zahir, Alamri, Abdullah, Khalil, Ibrahim, Zomaya, Albert Y., Foufou, Sebti, Bouras, Abdelaziz
Médium: Journal Article
Jazyk:angličtina
Vydáno: IEEE 01.09.2014
Institute of Electrical and Electronics Engineers
Témata:
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
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Shrnutí: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 similar according to specific metrics. There is a vast body of knowledge in the area of clustering and there has been attempts to analyze and categorize them for a larger number of applications. However, one of the major issues in using clustering algorithms for big data that causes confusion amongst practitioners is the lack of consensus in the definition of their properties as well as a lack of formal categorization. With the intention of alleviating these problems, this paper introduces concepts and algorithms related to clustering, a concise survey of existing (clustering) algorithms as well as providing a comparison, both from a theoretical and an empirical perspective. From a theoretical perspective, we developed a categorizing framework based on the main properties pointed out in previous studies. Empirically, we conducted extensive experiments where we compared the most representative algorithm from each of the categories using a large number of real (big) data sets. The effectiveness of the candidate clustering algorithms is measured through a number of internal and external validity metrics, stability, runtime, and scalability tests. In addition, we highlighted the set of clustering algorithms that are the best performing for big data.
ISSN:2168-6750
2168-6750
DOI:10.1109/TETC.2014.2330519