Construction of a Business Analysis GIS Basic Data Platform based on Hierarchical Clustering Algorithm

In the field of business analysis, the application of GIS (Geographic Information System) technology is becoming increasingly widespread, leading to an increasing demand for processing and analyzing large amounts of GIS data. The hierarchical clustering algorithm has received widespread attention due to its effectiveness in data classification and pattern recognition. However, when dealing with large-scale datasets, the efficiency and stability of this algorithm still face some challenges, which limit its application in commercial decision support systems. This article delves into the performance of hierarchical clustering algorithms on GIS data platforms. After a series of evaluations of the performance of the business analysis GIS basic data platform based on hierarchical clustering algorithms, it achieved an Adjusted Rand Index(AIR) of 0.96 on the standard test set. Based on the hierarchical clustering algorithm, the processing time for 10000 data points increased to 11.67 seconds. From the experimental data conclusion, it can be seen that although the hierarchical clustering algorithm performs well on small to medium-sized datasets, it faces significant challenges in processing large-scale datasets and high noise data. The findings of this paper provide valuable insights and guidance for future GIS platforms for business analytics.