Multiple kernel collaborative fuzzy clustering algorithm with weighted super-pixels for satellite image land-cover classification

2019 ◽  
Vol 85 ◽  
pp. 85-98 ◽  
Author(s):  
Trong Hop Dang ◽  
Dinh Sinh Mai ◽  
Long Thanh Ngo
Keyword(s):  
Land Cover ◽  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Satellite Image ◽  
Land Cover Classification ◽  
Fuzzy Clustering Algorithm ◽  
Multiple Kernel

scholarly journals A New Fuzzy Clustering Algorithm Based on Clonal Selection for Land Cover Classification

2011 ◽  
Vol 2011 ◽  
pp. 1-21 ◽  
Author(s):  
Yanfei Zhong ◽  
Liangpei Zhang
Keyword(s):  
Land Cover ◽  
Clustering Algorithm ◽  
Clonal Selection ◽  
Land Cover Classification ◽  
Optimal Number ◽  
Cluster Number ◽  
Number Of Clusters ◽  
Clonal Selection Theory ◽  
Fuzzy Clustering Algorithm ◽  
Optimal Number Of Clusters

A new fuzzy clustering algorithm based on clonal selection theory from artificial immune systems (AIS), namely, FCSA, is proposed to obtain the optimal clustering result of land cover classification withouta prioriassumptions on the number of clusters. FCSA can adaptively find the optimal number of clusters and is designed as a two-layer system: the classification layer and the optimization layer. The classification layer of FCSA, inspired by clonal selection theory, generates the optimal classification result with a fixed cluster number by utilizing the clone, mutation, and selection of immune operators. The optimization layer of FCSA evaluates the optimal solutions according to performance measures for cluster validity and then adjusts the cluster number to output the final optimal cluster number. Two experiments with different types of image evince that FCSA not only finds the optimal number of clusters, but also consistently outperforms the traditional clustering algorithms, such as K-means and Fuzzy C-means. Hence, FCSA provides an effective option for performing the task of land cover classification.

Transfer of knowledge in chemical equipment reliability

1989 ◽  
Vol 54 (10) ◽  
pp. 2692-2710 ◽  
Author(s):  
František Babinec ◽  
Mirko Dohnal
Keyword(s):  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Chemical Equipment ◽  
Transfer Of Knowledge ◽  
Equipment Reliability ◽  
Fuzzy Clustering Algorithm

The problem of transformation of data on the reliability of chemical equipment obtained in particular conditions to other equipment in other conditions is treated. A fuzzy clustering algorithm is defined for this problem. The method is illustrated on a case study.

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