A Density-center-based Automatic Clustering Algorithm for IoT Data Analysis

Tao Zhang, Meng Chu Zhou, Xiwang Guo, Liang Qi, Abdullah Abusorrah

Research output: Contribution to journalArticlepeer-review


With the rapid development of the Internet of Things (IoT), a large amount of data has been produced, and new requirements have been put forward for data mining. Clustering plays an essential role in discovering the underlying patterns of given IoT data. It is widely used in health prognoses, pattern recognition, information retrieval, image processing, and computer vision. Density clustering is crucial to find arbitrary-shaped clusters and noise points without knowing the number of clusters in advance. However, its efficiency and applicability are reduced sharply when there exists mutual interference among parameters. In this paper, a new algorithm called Density-center-based Automatic Clustering (DAC) is proposed. First, this work presents a non-parametric density computing method. Second, it proposes to use an adaptive neighborhood whose radius is automatically calculated based on all the points in a dataset. Finally, it selects appropriate density centers from a decision graph, which merge their surrounding points into the same groups. Experiments are conducted to show that the proposed DAC has higher accuracy than six classic and practical clustering algorithms. DAC has also shown strong effectiveness in analyzing data from photovoltaic power and oil extraction systems. Besides, as an outstanding feature that its compared peers lack, it can determine parameters automatically. As a result, this work advances the state-of-the-art of clustering algorithms in the field of IoT data analysis.

Original languageEnglish (US)
Pages (from-to)1
Number of pages1
JournalIEEE Internet of Things Journal
StateAccepted/In press - 2022

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Information Systems
  • Hardware and Architecture
  • Computer Science Applications
  • Computer Networks and Communications


  • Clustering algorithms
  • Clustering methods
  • Correlation
  • Data analysis
  • Density clustering
  • Density peaks
  • Internet of Things
  • Internet of Things
  • Neighborhood
  • Non-parametric
  • Self-organizing feature maps
  • Signal processing algorithms


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