2,025
Views
2
CrossRef citations to date
0
Altmetric
Research Article

A Cluster Analysis Methodology for the Categorization of Soil Samples for Forensic Sciences Based on Elemental Fingerprint

, , ORCID Icon & ORCID Icon
Article: 2010941 | Received 07 Apr 2021, Accepted 09 Nov 2021, Published online: 20 Dec 2021

Figures & data

Figure 1. Location of the study area and collected samples.

Figure 1. Location of the study area and collected samples.

Table 1. Descriptive variables of the northern Great Basin geochemical data set that were considered for analysis in this work

Table 2. Statistical measures (mean and standard deviation) for the concentrations of chemical components found in soil samples

Figure 2. Distribution of samples in the three clusters, divided by the K-means expressed by the two principal components. Cluster 1 = black circles, Cluster 2 = red triangles and Cluster 3 = green crosses.

Figure 2. Distribution of samples in the three clusters, divided by the K-means expressed by the two principal components. Cluster 1 = black circles, Cluster 2 = red triangles and Cluster 3 = green crosses.

Table 3. Mean and standard deviation values for the concentrations of the chemical elements in soil samples taken from the three clusters

Figure 3. Mean values for the concentration of the ten most relevant elements determined by the CFS feature selection technique.

Figure 3. Mean values for the concentration of the ten most relevant elements determined by the CFS feature selection technique.

Figure 4. Map from the location from which each sample was collected. Cluster 1 = gray, Cluster 2 = red and Cluster 3 = green.

Figure 4. Map from the location from which each sample was collected. Cluster 1 = gray, Cluster 2 = red and Cluster 3 = green.

Table 4. Accuracy values achieved by the SVM models developed on the original feature set and on the feature subset determined by the CFS. The best values for the model parameters computed by grid search are also shown