An improved xie-beni index for cluster validity measure

Singh, Munendra; Bhattacharjee, Romel; Sharma, Neeraj; Verma, Ashish

doi:10.1109/iciip.2017.8313691

Cited by 7 publications

(4 citation statements)

References 10 publications

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“… Cluster Metrics: Compute various internal cluster validation metrics for each combination of n_neighbors and leiden_resolution used in the grid search. Internal cluster validation metrics include within-cluster sum of squared error, within-cluster variance, Davies-Bouldin Index, Average Silhouette Score, Calinski-Harabasz Index (Pseudo-F statistic), and Xie-Beni Index ( Caliński and Harabasz, 1974 , Davies and Bouldin, 1979 , Rousseeuw, 1987 , Singh et al., 2017 ). Visualize internal cluster validation metrics on heatmaps and 3D surface plots.…”

Section: Discussionmentioning

confidence: 99%

Transcriptional profiling of mouse projection neurons with VECTORseq

Cheung¹,

Chung²,

Feinberg³

2022

STAR Protocols

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Section: Discussionmentioning

confidence: 99%

Transcriptional profiling of mouse projection neurons with VECTORseq

Cheung¹,

Chung²,

Feinberg³

2022

STAR Protocols

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“…Xie-Beni is an algorithm that functions to validate the compactness and separation of fuzzy clustering as explained by previous research that the classes resulting from the clustering process need to be validated based on the grouping indicators of the evaluation results in the form of the level of cohesiveness and the degree of separation [32]. Xie-Beni index can calculate the compactness and separation between fuzzy clusters [33], in order to when the Xie-Beni is applied to the clustering method, it can form an optimal cluster area.…”

Section: Literature Reviewmentioning

confidence: 99%

Determining rural development priorities using a hybrid clustering approach: a case study of South Sulawesi, Indonesia

2023

IJATEE

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South Sulawesi province implements a village assistance program. The program aims to ensure the successful implementation of village development activities, reduce urbanization, and address development problems in rural areas. This research discusses finding alternative solutions to determining the priority status of villages that need assistance from experts. Village assistance is an activity in the context of developing self-sufficiency and feasibility of

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“…where c p , c q , and c j denote the cluster centers, x i is any sample in the dataset, k is the number of clusters, and U is the sample set. e Xie-Beni (XB) index [45,46] is based on intracluster and intercluster distances; it is formulated in terms of the cluster compactness and separation between the clusters. We use the XB index for the evaluation of the cluster effects, and it is defined as follows:…”

Section: Experimental Designmentioning

confidence: 99%

A Clustering-based Method for Business Hall Efficiency Analysis

Huang

Wang

2021

Scientific Programming

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Excessive or insufficient business hall resources may result in unreasonable resource allocation, adversely affecting the value of an entity business hall. Therefore, proper characteristic parameters are the key factors for analyzing the business hall, which strongly affect the final analysis results. In this study, a characteristic analysis method for the economic operation of a business hall is developed and the feature engineering is established. Because of its simplicity and versatility, the k -means algorithm has been widely used since it was first proposed around 50 years ago. However, the classical k -means algorithm has poor stability and accuracy. In particular, it is difficult to achieve a suitable balance between of the centroid initialization and the clustering number k . We propose a new initialization (LSH- k -means) algorithm for k -means clustering. This algorithms is mainly based on locality-sensitive hashing (LSH) as an index for computing the initial cluster centroids, and it reduces the range of the clustering number. Furthermore, an empirical study is conducted. According to the load intensity and time change of the business hall, an index system reflecting the optimization analysis of the business hall is established, and the LSH- k -means algorithm is used to analyze the economic operation of the business hall. The results of the empirical study show that the LSH- k -means that the clustering method outperforms the direct prediction method, provides expected analysis results as well as decision optimization recommendations for the business hall, and serves as a basis for the optimal layout of the business hall.

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An improved xie-beni index for cluster validity measure

Cited by 7 publications

References 10 publications

Transcriptional profiling of mouse projection neurons with VECTORseq

Transcriptional profiling of mouse projection neurons with VECTORseq

Determining rural development priorities using a hybrid clustering approach: a case study of South Sulawesi, Indonesia

A Clustering-based Method for Business Hall Efficiency Analysis

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