Research on the Application of Improved K-Means Spatial Clustering Algorithm in Landscape Conservation of Gardens

Abstract

Garden art is the culture of generations since ancient times and is another spiritual carrier of people’s internal admiration for natural landscape, culture, and art as well as their love for living in nature and landscape. This article mainly studies the data clustering algorithm and adopts a research methodology that progresses from simple to complex. It starts by establishing a spatial data clustering model and then clustering the low-dimensional data, and then processing the high-dimensional spatial data in the low-dimensional data set. The original K-means clustering algorithm is then improved, and the new algorithm is created by combining PSO with the K-means algorithm in the high-dimensional spatial data set. And the improved two algorithms are applied to the study of data related to landscape conservation sites, and the powerful superiority of the improved K-means spatial clustering algorithm in this article is verified through the comparison of the algorithms.