Open AccessUsing fuzzy C-means Clustering to Identify Heavy Metal Polluted Soil in a Certain Area of Shanghai
Author(s) -
Yiping Xu,
Luolei Zhang,
Peng Yu,
Chongjin Zhao
Publication year - 2021
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.179
H-Index - 26
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/660/1/012092
Subject(s) - fuzzy clustering , cluster analysis , flame clustering , data mining , fuzzy logic , correlation clustering , mathematics , cure data clustering algorithm , pattern recognition (psychology) , artificial intelligence , computer science
Fuzzy clustering is an important branch of fuzzy pattern recognition, which has been widely used in many fields such as data mining, image processing, big data analysis and so on. Among many fuzzy clustering algorithms, fuzzy c-means algorithm is the most widely used. In this paper, the fuzzy c-means clustering is applied to the identification of polluted soil. To solve the problem of determining the optimal number of clusters for this method, the method we choose is obtaining an initial clustering result firstly and then merging. Based on the important characteristic of fuzzy c-means method that the objective function of fuzzy c-means clustering decreases rapidly as the number of clusters increases, and the rate become slow after exceeding the optimal number of clusters, we choose the clustering result whose objective function is reduced to a certain degree as the initial clustering result. Then use the parameter estimation method in statistics to estimate the distance between classes, determine the reasonable range of distance between classes, combine the initial classification results, and finally perform the final clustering results according to the clustering validity function. Evaluation and experiments prove the feasibility and effectiveness of the scheme.