Parallel algorithms for hierarchical clustering and cluster validity

1990 ◽  
Vol 12 (11) ◽  
pp. 1088-1092 ◽  
Author(s):  
X. Li
Keyword(s):  
Parallel Algorithms ◽  
Hierarchical Clustering ◽  
Cluster Validity

scholarly journals Towards Expert-Inspired Automatic Criterion to Cut a Dendrogram for Real-Industrial Applications

2021 ◽  
Author(s):  
Shikha Suman ◽  
Ashutosh Karna ◽  
Karina Gibert
Keyword(s):  
Hierarchical Clustering ◽  
Clustering Algorithms ◽  
Computational Cost ◽  
Real Life ◽  
Ground Truth ◽  
Industrial Applications ◽  
Underlying Structure ◽  
Cluster Validity ◽  
Cluster Validity Index ◽  
Number Of Clusters

Hierarchical clustering is one of the most preferred choices to understand the underlying structure of a dataset and defining typologies, with multiple applications in real life. Among the existing clustering algorithms, the hierarchical family is one of the most popular, as it permits to understand the inner structure of the dataset and find the number of clusters as an output, unlike popular methods, like k-means. One can adjust the granularity of final clustering to the goals of the analysis themselves. The number of clusters in a hierarchical method relies on the analysis of the resulting dendrogram itself. Experts have criteria to visually inspect the dendrogram and determine the number of clusters. Finding automatic criteria to imitate experts in this task is still an open problem. But, dependence on the expert to cut the tree represents a limitation in real applications like the fields industry 4.0 and additive manufacturing. This paper analyses several cluster validity indexes in the context of determining the suitable number of clusters in hierarchical clustering. A new Cluster Validity Index (CVI) is proposed such that it properly catches the implicit criteria used by experts when analyzing dendrograms. The proposal has been applied on a range of datasets and validated against experts ground-truth overcoming the results obtained by the State of the Art and also significantly reduces the computational cost.

Cluster Validity Measures Based Agglomerative Hierarchical Clustering for Network Data

2019 ◽  
Vol 23 (3) ◽  
pp. 577-583
Author(s):  
Yukihiro Hamasuna ◽  
Shusuke Nakano ◽  
Ryo Ozaki ◽  
and Yasunori Endo ◽  
◽  
...  
Keyword(s):  
Hierarchical Clustering ◽  
Optimal Number ◽  
Network Data ◽  
Network Clustering ◽  
Agglomerative Hierarchical Clustering ◽  
Cluster Validity ◽  
Validity Measures ◽  
Evaluation Measure ◽  
Network Partitions ◽  
Optimal Number Of Clusters

The Louvain method is a method of agglomerative hierarchical clustering (AHC) that uses modularity as the merging criterion. Modularity is an evaluation measure for network partitions. Cluster validity measures are also used to evaluate cluster partitions and to determine the optimal number of clusters. Several cluster validity measures are constructed considering the geometric features of clusters. These measures and modularity are considered to be the same concept in the viewpoint of evaluating cluster partitions. In this paper, cluster validity measures based agglomerative hierarchical clustering (CVAHC) is proposed as a novel clustering method for network data. The cluster validity measures are used as a merging criterion and an evaluation measure for network data in the proposed method. Numerical experiments show that Dunn’s and Xie-Beni’s indices for network partitions are useful for network clustering.

SEP/COP: An efficient method to find the best partition in hierarchical clustering based on a new cluster validity index

2010 ◽  
Vol 43 (10) ◽  
pp. 3364-3373 ◽  
Author(s):  
Ibai Gurrutxaga ◽  
Iñaki Albisua ◽  
Olatz Arbelaitz ◽  
José I. Martín ◽  
Javier Muguerza ◽  
...  
Keyword(s):  
Hierarchical Clustering ◽  
Efficient Method ◽  
Cluster Validity ◽  
Cluster Validity Index ◽  
Validity Index
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