https://doi.org/10.5281/zenodo.21572262

Thus, an increasing number of data and their increasing complexity require new approaches for clustering large data sets in many fields. This paper aims to present a Scalable Hybrid Clustering Framework referred to as SHC-PPSO in this paper based on Particle Swarm Optimization. It advances greatly. It is evident from the parallel processing and PSO that SHC-PPSO optimally converges and excels median attribute methods. This is faster than when data has to be analyzed sequentially, which reduces the likelihood of making mistakes. For reducing the curse of “Dimensality curse” actually, SHC-PPSO truncated the processing power. Some MOA hybrid clustering algorithm enhancements increase pattern recognition clearer and faster over the mean and the similarity distance methods. It was clearly observed that proposed SHC-PPSO algorithm provided better results than SCPSO-F1, SCPSO-F2 and PSOGSA with 65 & 95 % accuracy on HIGGS and CICIDS2017 dataset respectively. SHC PPSO of distortions also enhanced the performance and runtime. On the HIGGS dataset the overall running time was decreased from 15000 seconds for a single node to 500 for 32 nodes while the speedup almost followed the linear growth up to 30 for 32 nodes. These data demonstrate that the system operates effectively and can be generalized to other circumstances. Other areas that have benefited from SHC-PPSO are rivalry mapping, biology, market categorization, and expansive network glaring violations detection. To achieve efficiency for large, complex OBO datasets, parallelization is done on precision clustering. With the help of computer clustering the approach to data analysis and its interpretation could be changed radically.