Author(s)

Jiaogen Zhou¹, Yang Wang^1,2

¹School of Urban and Environmental Science, Huaiyin Normal University, Huai’an, China.
²Information Engineering School, Nanchang University, Nanchang, China.

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.

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

Zhou, J. and Wang, Y. (2019) Different Feature Selection of Soil Attributes Influenced Clustering Performance on Soil Datasets. International Journal of Geosciences, 10, 919-929. doi: 10.4236/ijg.2019.1010052.