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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.
Thescientificworldjournal
|August 12, 2014
Summary
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.
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.
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