A Review of Clustering Algorithms for Big Data

Big data is usually defined by five (05) characteristics called 5Vs +1C (Volume, Velocity, Variety, Veracity, Value and Complexity). It means to data that are too large, dynamic and complex with certain degree of accuracy. For that, data become difficult to analyze using traditional data analysis techniques because of their high complexity and computational cost. Clustering analysis technique is the most used method for cope with huge amount of data. The main goal of clustering is to classify data into clusters in manner that data grouped are more similar. In this paper, we provide an overview of various clustering techniques used for data analysis.

• Publication year

  2019

• Venue

  2019 International Conference on Networking and Advanced Systems (ICNAS)

• Publication date

  2019-06-01

• Fields of study

  Mathematics, Computer Science

• Identifiers
  DOI 10.1109/ICNAS.2019.8807822
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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