Different Feature Selection of Soil Attributes Influenced Clustering Performance on Soil Datasets  

作　　者：Jiaogen Zhou Yang Wang 

机构地区：[1]School of Urban and Environmental Science, Huaiyin Normal University, Huai’an, China [2]Information Engineering School, Nanchang University, Nanchang, China.

出　　处：《International Journal of Geosciences》2019年第10期919-929,共11页地球科学国际期刊（英文）

摘　　要：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 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.

关 键 词：FEATURE Selection K-MEANS CLUSTERING Fuzzy C-MEANS CLUSTERING Spectral CLUSTERING SOIL Attributes 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]