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.
Highlights
Clustering generally divides a dataset into k sub-clusters such that similar objects are within the same sub-cluster and dissimilar objects are in different sub-clusters [1]
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
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
Summary
Clustering generally divides a dataset (in which each data object has certain attributes) into k sub-clusters such that similar objects are within the same sub-cluster and dissimilar objects are in different sub-clusters [1]. To obtain good clustering results, we normally require relevant features to be included in the training data and an appropriate clustering method
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