Development of a local density optimization approach for structure improvement and cluster separation in water quality data - Hasugian, Paska Marto; Simangunsong, Pandi Barita Nauli; Sipayung, Sardo Pardingotan (Eastern-European Journal of Enterprise Technologies, 2025)
The object of this study is the clustering of water quality data characterized by complex distribution patterns, irregular cluster shapes, and local density variations. The main problem encountered is the limitation of conventional methods such as K-means in achieving optimal cluster separation when the data has uneven distribution, overlap between clusters, and density imbalance. To overcome this, a clustering approach based on local density optimization (LDO) was developed, integrated with principal component analysis (PCA) for dimension reduction and Pasca distance (PaDi) to adjust distance calculations according to local density variations. In this approach, LDO serves to improve data distribution by maintaining global topology and local density consistency before performing cluster formation using the K-means algorithm. Testing on a real water quality dataset shows that the combination of PCA + LDO + PaDi + K-means achieves a Silhouette score of 0.3450, a Davies-Bouldin index of 0.9149, and a Calinski-Harabasz Index of 616.1674, which is superior to both standard K-means and PCA + K-means. This improvement was achieved due to the LDO’s ability to reduce density distortion, resulting in more compact clusters, clearer boundaries, and reduced classification errors in transition areas. The proposed approach is characterized by adaptive density-based transformation, sensitivity to local variations through PaDi, and high stability in iterations, ensuring robustness in diverse data conditions. Thus, this approach is relevant for large-scale and real-time water quality monitoring systems and can be extended to other multidimensional datasets in the environmental, industrial, and ecological fields with complex distributions, providing a strong analytical basis for decision-making and policy development