A New Fuzzy Clustering Validity Index With a Median Factor for Centroid-Based Clustering

2015 ◽  
Vol 23 (3) ◽  
pp. 701-718 ◽  
Author(s):  
Chih-Hung Wu ◽  
Chen-Sen Ouyang ◽  
Li-Wen Chen ◽  
Li-Wei Lu
Keyword(s):  
Fuzzy Clustering ◽  
Validity Index ◽  
Clustering Validity Index ◽  
Clustering Validity

scholarly journals A Validity Index for Fuzzy Clustering Based on Bipartite Modularity

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Yongli Liu ◽  
Xiaoyang Zhang ◽  
Jingli Chen ◽  
Hao Chao
Keyword(s):  
Fuzzy Clustering ◽  
Optimal Number ◽  
Experimental Results ◽  
Validity Index ◽  
Number Of Clusters ◽  
Validity Indices ◽  
Noise Data ◽  
Clustering Validity ◽  
Optimal Number Of Clusters

Because traditional fuzzy clustering validity indices need to specify the number of clusters and are sensitive to noise data, we propose a validity index for fuzzy clustering, named CSBM (compactness separateness bipartite modularity), based on bipartite modularity. CSBM enhances the robustness by combining intraclass compactness and interclass separateness and can automatically determine the optimal number of clusters. In order to estimate the performance of CSBM, we carried out experiments on six real datasets and compared CSBM with other six prominent indices. Experimental results show that the CSBM index performs the best in terms of robustness while accurately detecting the number of clusters.

scholarly journals A Self-Adaptive Fuzzyc-Means Algorithm for Determining the Optimal Number of Clusters

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Min Ren ◽  
Peiyu Liu ◽  
Zhihao Wang ◽  
Jing Yi
Keyword(s):  
Adaptive Method ◽  
Convergence Result ◽  
Decision Function ◽  
Optimal Number ◽  
Validity Index ◽  
Number Of Clusters ◽  
Clustering Validity Index ◽  
Clustering Validity ◽  
Optimal Number Of Clusters ◽  
Self Adaptive

For the shortcoming of fuzzyc-means algorithm (FCM) needing to know the number of clusters in advance, this paper proposed a new self-adaptive method to determine the optimal number of clusters. Firstly, a density-based algorithm was put forward. The algorithm, according to the characteristics of the dataset, automatically determined the possible maximum number of clusters instead of using the empirical rulenand obtained the optimal initial cluster centroids, improving the limitation of FCM that randomly selected cluster centroids lead the convergence result to the local minimum. Secondly, this paper, by introducing a penalty function, proposed a new fuzzy clustering validity index based on fuzzy compactness and separation, which ensured that when the number of clusters verged on that of objects in the dataset, the value of clustering validity index did not monotonically decrease and was close to zero, so that the optimal number of clusters lost robustness and decision function. Then, based on these studies, a self-adaptive FCM algorithm was put forward to estimate the optimal number of clusters by the iterative trial-and-error process. At last, experiments were done on the UCI, KDD Cup 1999, and synthetic datasets, which showed that the method not only effectively determined the optimal number of clusters, but also reduced the iteration of FCM with the stable clustering result.

Color image segmentation using genetic algorithm with aggregation-based clustering validity index (CVI)

2019 ◽  
Vol 13 (5) ◽  
pp. 833-841 ◽  
Author(s):  
Ahmad Khan ◽  
Zia ur Rehman ◽  
Muhammad Arfan Jaffar ◽  
Javid Ullah ◽  
Ahmad Din ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Image Segmentation ◽  
Color Image ◽  
Color Image Segmentation ◽  
Validity Index ◽  
Clustering Validity Index ◽  
Clustering Validity
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