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

7 Scopus citations


With the rapid development of Internet of Things (IoT), much data has been produced, and new requirements have been posed for data mining. Clustering plays an essential role in discovering the underlying patterns of IoT data. It is widely used in health prognoses, pattern recognition, information retrieval, 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 article, a new algorithm called density-center-based automatic clustering (DAC) is proposed. First, this work presents a nonparametric density computing method. Second, it proposes to use an adaptive neighborhood whose radius is automatically calculated based on all the points in a data set. 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 DAC has higher accuracy than six classic and updated algorithms. Its effectiveness is shown via data from photovoltaic power and oil extraction systems. As an outstanding feature that its compared peers lack, it can determine parameters automatically. Thus this work greatly advances the state-of-the-art of clustering algorithms in the field of IoT data analysis.

Original languageEnglish (US)
Pages (from-to)24682-24694
Number of pages13
JournalIEEE Internet of Things Journal
Issue number24
StatePublished - Dec 15 2022

All Science Journal Classification (ASJC) codes

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


  • Density clustering
  • Internet of Things (IoT)
  • density peaks
  • neighborhood
  • nonparametric


Dive into the research topics of 'A Density-Center-Based Automatic Clustering Algorithm for IoT Data Analysis'. Together they form a unique fingerprint.

Cite this