Araujo, S.R., Wetterlind, J., Dematte, J.A.M. and Stenberg, B. (2014) Improving the Prediction Performance of a Large Tropical vis-NIR Spectroscopic Soil Library from Brazil by Clustering into Smaller Subsets or Use of Data Mining Calibration Techniques. European Journal of Soil Science, 65, 718-729.
https://doi.org/10.1111/ejss.12165

has been cited by the following article:

• TITLE: Different Feature Selection of Soil Attributes Influenced Clustering Performance on Soil Datasets

  AUTHORS: Jiaogen Zhou, Yang Wang

  KEYWORDS: Feature Selection, K-Means Clustering, Fuzzy C-Means Clustering, Spectral Clustering, Soil Attributes

  JOURNAL NAME: International Journal of Geosciences, Vol.10 No.10, October 21, 2019

  ABSTRACT: Feature selection is very important to obtain meaningful and interpretive clustering results from a clustering analysis. In the application of soil data clustering, there is a lack of good understanding of the response of clustering performance to different features subsets. In the present paper, we analyzed the performance differences between k-means, fuzzy c-means, and spectral clustering algorithms in the conditions of different feature subsets of soil data sets. The experimental results demonstrated that the performances of spectral clustering algorithm were generally better than those of k-means and fuzzy c-means with different features subsets. The feature subsets containing environmental attributes helped to improve clustering performances better than those having spatial attributes and produced more accurate and meaningful clustering results. Our results demonstrated that combination of spectral clustering algorithm with the feature subsets containing environmental attributes rather than spatial attributes may be a better choice in applications of soil data clustering.