Parallel and scalable Dunn Index for the validation of big data clusters

N’cir, Chiheb-Eddine Ben; Hamza, Abdallah; Bouaguel, Waad

doi:10.1016/j.parco.2021.102751

Cited by 23 publications

(10 citation statements)

References 12 publications

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“…It returns a single number indicating the level of accuracy of clustering findings. The Dunn index seeks to maximize the ratio of inter-clustering distance to intra-clustering separation [110]. The Dunn index is employed as an assessment metric in clustering as follows.…”

Section: Dunn Indexmentioning

confidence: 99%

A Systematic Literature Review on Identifying Patterns Using Unsupervised Clustering Algorithms: A Data Mining Perspective

Chaudhry,

Shafi,

Mahnoor

et al. 2023

Symmetry

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Data mining is an analytical approach that contributes to achieving a solution to many problems by extracting previously unknown, fascinating, nontrivial, and potentially valuable information from massive datasets. Clustering in data mining is used for splitting or segmenting data items/points into meaningful groups and clusters by grouping the items that are near to each other based on certain statistics. This paper covers various elements of clustering, such as algorithmic methodologies, applications, clustering assessment measurement, and researcher-proposed enhancements with their impact on data mining thorough grasp of clustering algorithms, its applications, and the advances achieved in the existing literature. This study includes a literature search for papers published between 1995 and 2023, including conference and journal publications. The study begins by outlining fundamental clustering techniques along with algorithm improvements and emphasizing their advantages and limitations in comparison to other clustering algorithms. It investigates the evolution measures for clustering algorithms with an emphasis on metrics used to gauge clustering quality, such as the F-measure and the Rand Index. This study includes a variety of clustering-related topics, such as algorithmic approaches, practical applications, metrics for clustering evaluation, and researcher-proposed improvements. It addresses numerous methodologies offered to increase the convergence speed, resilience, and accuracy of clustering, such as initialization procedures, distance measures, and optimization strategies. The work concludes by emphasizing clustering as an active research area driven by the need to identify significant patterns and structures in data, enhance knowledge acquisition, and improve decision making across different domains. This study aims to contribute to the broader knowledge base of data mining practitioners and researchers, facilitating informed decision making and fostering advancements in the field through a thorough analysis of algorithmic enhancements, clustering assessment metrics, and optimization strategies.

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Section: Dunn Indexmentioning

confidence: 99%

A Systematic Literature Review on Identifying Patterns Using Unsupervised Clustering Algorithms: A Data Mining Perspective

Chaudhry,

Shafi,

Mahnoor

et al. 2023

Symmetry

View full text Add to dashboard Cite

show abstract

“…Another statistic might be employed when evaluating clustering techniques, like DI [38]. The formula for calculating DI is dividing the shortest distance between clusters by the most significant size possible.…”

Section:  Dunn's Index(di)mentioning

confidence: 99%

Analysis of Unsupervised Machine Learning Techniques for an Efficient Customer Segmentation using Clustering Ensemble and Spectral Clustering

Hicham¹,

Karim²

2022

IJACSA

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Customer segmentation is key to a corporate decision support system. It is an important marketing technique that can target specific client categories. We create a novel consumer segmentation technique based on a clustering ensemble; in this stage, we ensemble four fundamental clustering models: DBSCAN, K-means, Mini Batch K-means, and Mean Shift, to deliver a consistent and high-quality conclusion. Then, we use spectral clustering to integrate numerous clustering findings and increase clustering quality. The new technique is more flexible with client data. Feature engineering cleans, processes, and transforms raw data into features. These traits are then used to form clusters. Adjust Rand Index (ARI), Normalized Mutual Information (NMI), Dunn's Index (DI), and Silhouette Coefficient (SC) were utilized to evaluate our model's performances with individual clustering approaches. The experimental analysis found that our model has the best ARI (70.14%), NMI (71.75), DI (75.15), and SC (72.89%). After retaining these results, we applied our model to an actual dataset obtained from Moroccan citizens via social networks and email boxes between 03/06/2022 and 19/08/2022.

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“…Dunn Index: this cluster index is primed towards providing a high score for clusters that are compact with minimal variance within members of the same cluster, with a maximal separation between classes (Ben Ncir et al, 2021;Bezdek & Pal, 1995;Legány et al, 2006). The main limitation of this clustering index is the scaling of computational cost associated with dealing with high dimensional data (Ben Ncir et al, 2021;Bezdek & Pal, 1995;Legány et al, 2006).…”

Section: -Cluster Validity Indexmentioning

confidence: 99%

Toward a Self-Supervised Architecture for Semen Quality Prediction Using Environmental and Lifestyle Factors

Nsugbe

2022

AIA

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Male fertility has been seen to be declining, prompting for more effective and accessible means of its assessment. AI methods have been effective towards predicting semen quality through a questionnaire-based information source comprising a selection of factors from the medical literature which have been seen to influence semen quality. Prior work has seen the application of supervised learning towards the prediction of semen quality, but since supervised learning hinges on the provision of data class labels it can be said to depend on an external intelligence intervention, which can translate towards further costs and resources in practical settings. In contrast, unsupervised learning methods partition data into clusters and groups based on an objective function and do not rely on the provision of class labels, and can allow for a fully automated flow of a prediction platform. In this paper we apply three unsupervised learning models with different model architectures, namely: Gaussian mixture model (GMM), K-means and spectral clustering (SC), alongside low dimensional embedding methods which include sparse autoencoder (SAE), principal component analysis (PCA) and robust principal component analysis (RPCA). The best results were obtained with a combination of the sparse autoencoder and the SC algorithm, which was likely due to its nonspecific and arbitrary cluster shape assumption. Further work would now involve the exploration of similar unsupervised learning algorithms with a similar framework to the SC to investigate the extent to which various clusters can be learned with maximal accuracy.

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Parallel and scalable Dunn Index for the validation of big data clusters

Cited by 23 publications

References 12 publications

A Systematic Literature Review on Identifying Patterns Using Unsupervised Clustering Algorithms: A Data Mining Perspective

A Systematic Literature Review on Identifying Patterns Using Unsupervised Clustering Algorithms: A Data Mining Perspective

Analysis of Unsupervised Machine Learning Techniques for an Efficient Customer Segmentation using Clustering Ensemble and Spectral Clustering

Toward a Self-Supervised Architecture for Semen Quality Prediction Using Environmental and Lifestyle Factors

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