Towards an efficient real-time kernel function stream clustering method via shared nearest-neighbor density for the IIoT - Huang, Ruohe; Xiao, Ruliang; Zhu, Weifu; Gong, Ping; Chen, Jinhui; Rida, Imad (Information Sciences, 2021)
•The Euler kernel function is used to map the data to the complex feature space.•The shared-neighbor density is used to divide the micro-clusters.•This paper proposes a novel projection data stream summary structure.•Relearning strategy is used to reduce the misjudgment of outliers for fusion sets. The rapid development of 5G communication technology will considerably help the expansion and gth Industrial Internet of things (IIoT). Indeed, both the data scale and dimension will significantly increase, leading to a challenging problem of the effective real-time stream clustering in the field of IIoT streaming mining. This paper proposes an efficient and novel real-time kernel function stream clustering method based on shared nearest-neighbor density for IIoT. In the proposed method, the projection technology is used to select the dimensions of high-dimensional data, while the Euler kernel function is used as the similarity measure. Furthermore, the micro-clusters are divided by the shared nearest-neighbor density, and the outliers are relearned. The main innovation lies in using the Euler kernel function to measure the similarity, reduce the sensitivity of outliers, and use the relearning strategy to improve the clustering quality of the data stream. The theoretical analysis and experimental comparisons on the simulated data sets show that the proposed method is very effective and represents a good solution for clustering real-time data streams of IIoT.