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Comprehensive Analysis & Performance Comparison of Clustering Algorithms for Big Data

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  • Anand Nayyar
  • Vikram Puri

Abstract

21st Century has marked high velocity of data generation not only in terms of size but also in variety. Analyzing large data sets with different forms is also a challenging task. Data Mining is regarded as efficient method to extract meaningful information as per user requirements. But considering the size of modern data, traditional data mining techniques are failing. Clustering can be regarded as one of the most important technique to mine the data by splitting large data sets into clusters. The paper’s primary contribution is to provide comprehensive analysis of Big Data Clustering algorithms on basis of: Partitioning, Hierarchical, Density, Grid and Model. In addition to this, performance comparison of algorithms is performed on basis of volume, variety and velocity.

Suggested Citation

  • Anand Nayyar & Vikram Puri, 2017. "Comprehensive Analysis & Performance Comparison of Clustering Algorithms for Big Data," Review of Computer Engineering Research, Conscientia Beam, vol. 4(2), pages 54-80.
    • Handle: RePEc:pkp:rocere:v:4:y:2017:i:2:p:54-80:id:1454
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    Cited by:

    1. Amitay Kligman & Arbel Yaniv & Yuval Beck, 2023. "Energy Disaggregation of Type I and II Loads by Means of Birch Clustering and Watchdog Timers," Energies, MDPI, vol. 16(7), pages 1-21, March.
    2. Kuo, Pei-Fen & Brawiswa Putra, I Gede & Setiawan, Faizal Azmi & Wen, Tzai-Hung & Chiu, Chui-Sheng & Sulistyah, Umroh Dian, 2022. "The impact of the COVID-19 pandemic on O-D flow and airport networks in the origin country and in Northeast Asia," Journal of Air Transport Management, Elsevier, vol. 100(C).

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