A novel cluster validity index for fuzzy clustering based on bipartite modularity

Zhang, Dawei; Ji, Min; Yang, Jun; Zhang, Yong; Xie, Fuding

doi:10.1016/j.fss.2013.12.013

Cited by 39 publications

(15 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Then, cluster validity indices are used to find the best partitioning of data. A great number of such indices have been introduced, e.g., [3,9,10,13,17,26,28,30,31]. In many validity indices two properties of clusters are taken into account, i.e., compactness and separability [11].…”

Section: Introductionmentioning

confidence: 99%

A new validity index for crisp clusters

Starczewski

2015

Pattern Anal Applic

View full text Add to dashboard Cite

In this paper, a new cluster validity index which can be considered as a measure of the accuracy of the partitioning of data sets is proposed. The new index, called the STR index, is defined as the product of two components which determine changes of compactness and separability of clusters during a clustering process. The maximum value of this index identifies the best clustering scheme. Three popular algorithms have been applied as underlying clustering techniques, namely complete-linkage, expectation maximization and K-means algorithms. The performance of the new index is demonstrated for several artificial and real-life data sets. Moreover, this new index has been compared with other well-known indices, i.e., Dunn, Davies-Bouldin, PBM and Silhouette indices, taking into account the number of clusters in a data set as the comparison criterion. The results prove superiority of the new index as compared to the above-mentioned indices.

show abstract

Section: Introductionmentioning

confidence: 99%

A new validity index for crisp clusters

Starczewski

2015

Pattern Anal Applic

View full text Add to dashboard Cite

show abstract

“…However, it causes high time complexity. Zhang et al [90] proposed a weighted bipartite network based fuzzy validity index (WGLI) to determine the number of clusters, and the maximum value of WGLI in the evaluation graph was determined as the number of clusters. In contrast to the above presented studies which apply K-means, hierarchical and EM algorithms, FCM was chosen as a clustering algorithm in this study.…”

Section: Traditional Approachesmentioning

confidence: 99%

A comprehensive survey of traditional, merge-split and evolutionary approaches proposed for determination of cluster number

Hançer

Karaboğa

2017

Swarm and Evolutionary Computation

108

View full text Add to dashboard Cite

“…Rezaee proposed a new validity index for GK algorithm to overcome the shortcomings of Kim's index [21]. Zhang et al proposed a novel WGLI to detect the optimal cluster number, using global optimum membership as the global property and modularity of bipartite network as the local independent property [22]. The clustering validity index based on the geometric structure of the dataset considers both the fuzzy degree of membership and the geometric structure, but its membership function is quite complicated with large calculating amount.…”

Section: Related Workmentioning

confidence: 99%

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

Ren

Liu

Wang

et al. 2016

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

For the shortcoming of fuzzy c-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 rule n and 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.

show abstract

A novel cluster validity index for fuzzy clustering based on bipartite modularity

Cited by 39 publications

References 28 publications

A new validity index for crisp clusters

A new validity index for crisp clusters

A comprehensive survey of traditional, merge-split and evolutionary approaches proposed for determination of cluster number

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

Contact Info

Product

Resources

About