Investigating Clustering Algorithms For Partial Object Classification Issues Utilizing Grid Dbscan Method for Spatial Data Analysis

Abstract

This paper investigates the effectiveness of clustering algorithms for solving partial object classification issues in spatial data analysis. To this end, the Grid-DBSCAN algorithm is proposed as an efficient clustering technique for solving partial object classification problems. The Grid-DBSCAN algorithm is based on the DBSCAN algorithm and incorporates a grid-based approach to improve its performance. The algorithm is tested on several real-world datasets and compared to other clustering algorithms. The experimental results demonstrate that the Grid-DBSCAN algorithm outperforms the other clustering algorithms in terms of accuracy and robustness, and is capable of finding the optimal solution for partial object classification tasks. Furthermore, the Grid-DBSCAN algorithm can be extended to handle other types of complex datasets. This paper provides an insight into the effectiveness of the proposed algorithm and its potential to solve partial object classification tasks in spatial data analysis.