A New Clustering Algorithm and Its Application in Assessing the Quality of Underground Water

Abstract

Cluster analysis, which is to partition a dataset into groups so that similar elements are assigned to the same group and dissimilar elements are assigned to different ones, has been widely studied and applied in various fields. The two challenging tasks in clustering are determining the suitable number of clusters and generating clusters of arbitrary shapes. This paper proposes a new concept of “epsilon radius neighbors” which plays an essential role in the cluster-forming process, thereby determining both the number of clusters and the shape of clusters, automatically. Based on “epsilon radius neighbors,” a new clustering algorithm in which the epsilon radius value is adapted to the characteristics of each cluster in the current partition is proposed. Recently, clustering has been widely applied in environmental applications, including underground water quality monitoring. However, the existing studies have simply applied conventional clustering techniques, in which the abovementioned two challenging tasks have not been solved already. Therefore, in this paper, the proposed clustering algorithm is applied in assessing the underground water quality in Phu My Town, Ba Ria-Vung Tau Province, Vietnam. The experimental results on benchmark datasets demonstrate the effectiveness of the proposed algorithm. For the quality of underground water, the new algorithm results in four clusters with different characteristics. Through this application, we found that the new algorithm might provide valuable reference information for underground water management.

Water quality monitoring and assessment based on cruise monitoring, remote sensing, and deep learning: A case study of Qingcaosha Reservoir

“…In particular, when faced with the huge and complex matrix of water quality attributes formed by the establishment of a big data platform like this study, making a meaningful water quality assessment is often difficult Singh et al (2005). A cluster analysis can be applied to interpret these complex data matrices to help understand the water quality and ecological status of the studied systems, identifying the possible resources and finding rapid solutions to pollution problems by grouping the data so that similar elements are assigned to the same group and different elements are assigned to different ones Vo- Van et al (2020); Simeonov et al (2003). Additionally, considering that deep clustering is more effective at analyzing big data than traditional clustering methods Guo et al (2017), such as K-means and C-means, an advanced deep learning clustering algorithm, improved deep embedded clustering (IDEC), was used for the water quality assessment in this study.…”

Scientific Programming Tools for Water Management

“…e paper "A New Clustering Algorithm and Its Application in Assessing the Quality of Underground Water" [1] introduces a new concept of "epsilon radius neighbours" in cluster analysis. Based on "epsilon radius neighbours," a new clustering algorithm in which the epsilon radius value is adapted to the characteristics of each cluster in the current partition is proposed for groundwater quality monitoring.…”

Density Peaks Clustering by Zero-Pointed Samples of Regional Group Borders

“…As shown in Figure 1, there are two clusters A and B in a dataset, and cluster B is misclassified into B1, B2, and B3. e elements I, 7 and 8, and II, 16,17,18,19,20, and 21, are marked with red wireframes. ey belong to NCB.…”

“…Compared with supervised learning [2][3][4][5][6][7][8][9][10][11][12][13][14][15][16], it can carry out the grouping task even though the category labels are pending. Hence, it is widely used in image segmentation [17], bioinformatics [18], pattern recognition [19], data mining [20], and other fields [21,22]. Representative clustering algorithms cover K-means [23,24] and fuzzy c-means [25,26] based on partitioning; AGNES [27], BIRCH [28,29], and CURE [30,31] based on hierarchy; DBSCAN [32] and OPTICS [33] based on density; STING [34] based on grids; and statistical clustering CMM [35] and spectral clustering [36] based on graph theory [37].…”