Paper Info
Title
Fuzzy C-Means clustering based on dual expression between cluster prototypes and reconstructed data.
Abstract
The Fuzzy C-Means (FCM) algorithm is one of the most commonly used clustering methods. In this study, the reconstructed data supervised by the original data is introduced into the FCM clustering, and a dual expression between cluster prototypes and reconstructed data is mined by extending the FCM clustering model using cluster prototypes, memberships and reconstructed data as variables. The convergence and the time complexity of the proposed algorithm are also discussed. Experiments using synthetic data sets and real-world data sets are focused on the influence of the extent to which the reconstructed data are supervised by the original data on the clustering performance. A way of parameter selection is provided which is helpful for enhancing the usefulness of the proposed algorithm. An application case study for monitoring data of shield construction is also presented. It reveals the effectiveness of the proposed algorithm from the viewpoints of the interpretability of clustering results and the representativeness of cluster prototypes.
Year
DOI
Venue
2017
10.1016/j.ijar.2017.08.008
International Journal of Approximate Reasoning
Keywords
Field
DocType
Fuzzy clustering,Fuzzy C-Means,Reconstructed data,Dual expression,Parameter selection
Fuzzy clustering,Data mining,CURE data clustering algorithm,Computer science,Artificial intelligence,FLAME clustering,Cluster analysis,k-medians clustering,Canopy clustering algorithm,Clustering high-dimensional data,Pattern recognition,Correlation clustering,Machine learning
Journal
Volume
Issue
ISSN
90
1
0888-613X
Citations 
PageRank 
References 
4
0.49
21
Authors
6
Name
Order
Citations
PageRank
Liyong Zhang16913.52
Wan-Xie Zhong2154.69
Chongquan Zhong3526.34
Wei Lu4493.37
Xiaodong Liu549228.50
W. Pedrycz6139661005.85