Application Fuzzy Logic for Clustering Big Data Sets

The rapid growth of industrial data generated by modern SCADA systems in manufacturing requires advanced Big Data frameworks capable of handling large-scale, real-time analytics. Apache Spark has emerged as one of the most efficient platforms, offering execution speeds up to 100 times faster than MapReduce. In spiral steel pipe production, sensor-rich SCADA environments generate high dimensional and continuous data streams that demand clustering algorithms which are both time efficient and space-efficient. This paper introduces SRSIO-FCM, a scalable partitioning fuzzy clustering algorithm, specifically designed to address the challenges of Big Data clustering in industrial settings. Implemented on the Apache Spark platform, the proposed SRSIO-FCM is evaluated against SLFCM, a scalable version of the Literal Fuzzy c-Means (LFCM) algorithm. The evaluation employs F-measure, Adjusted Rand Index (ARI), objective function value (OFV), and execution time to assess performance on large-scale SCADA datasets. The experimental results confirm that SRSIO-FCM delivers superior clustering accuracy and significantly reduced execution time compared to SLFCM, proving its capability for real-time monitoring and predictive analytics in spiral steel pipe manufacturing.