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A fast density-based clustering algorithm for real-time Internet of Things stream.

Amineh Amini1, Hadi Saboohi1, Teh Ying Wah1

  • 1Department of Information System, Faculty of Computer Science and Information Technology, University of Malaya, 50603 Kuala Lumpur, Malaysia.

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Summary
This summary is machine-generated.

This study introduces a fast density-based clustering algorithm for Internet of Things (IoT) data streams. It efficiently discovers trends and patterns in real-time, improving organizational value.

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Area of Science:

  • Data Science
  • Internet of Things (IoT)
  • Machine Learning

Background:

  • Continuous data streams from IoT devices require rapid analysis for trend discovery and value creation.
  • Density-based clustering is effective for IoT streams due to its ability to handle arbitrary shapes and outliers without predefining cluster numbers.

Purpose of the Study:

  • To propose a novel density-based clustering algorithm specifically designed for high-speed processing of IoT data streams.
  • To address the challenge of density-based clustering within limited time constraints for real-time IoT applications.

Main Methods:

  • Developed a new density-based clustering algorithm optimized for speed.
  • Evaluated the algorithm's performance on both real and synthetic IoT data streams.

Main Results:

  • The proposed algorithm demonstrates fast processing times, making it suitable for real-time IoT applications.
  • Experimental results confirm high-quality clustering outcomes with low computational overhead.

Conclusions:

  • The developed density-based clustering algorithm effectively handles IoT data streams in real-time.
  • The approach offers a valuable solution for extracting insights from high-velocity IoT data.