An Architectural Optimization Framework for Scalable Spatial Clustering in High-Redundancy Environments

Spatial Big Data mining is often hindered by high computational complexity and the intrinsic autocorrelation of georeferenced records. To address these challenges, this study proposes an architectural optimization framework for the CHSMST+ algorithm, designated as CHSMST+MR. Rather than introducing a brand-new clustering paradigm, the framework focuses on a Distributed Spatial Cardinality Reduction (DSCR) layer that aggregates redundant spatial records before the core iterative mining logic begins. By transforming raw records into a weighted key-value representation within the Apache Spark environment, the proposed approach significantly mitigates the shuffling bottleneck common in distributed systems. Experimental validation using high-density biological datasets demonstrates an average execution-time reduction of 51.36%, with performance gains reaching up to 79.96% in specific high-redundancy scenarios. The results, obtained through controlled local emulation, confirm that this architectural optimization provides a scalable, deterministic, and lossless solution for accelerating spatial clustering. This work contributes a methodological path for enhancing the performance of iterative spatial mining algorithms in environments characterized by massive data density and coordinate redundancy.