Study of Document Clustering Algorithms Applied on Covid Data

Suresh, Sweety; Krishna, G. Rama; Thushara, MG

doi:10.1007/978-981-19-4676-9_27

Cited by 1 publication

(2 citation statements)

References 21 publications

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“…There is virtually little commonality across the clusters because each cluster's members are similar 1,2 . Assigning labels to samples that identify each sample's membership in the cluster is the goal of clustering 3 . The clustering outcomes for the same data can vary because different clustering methods employ varied similarity/distance measuring strategies.…”

Section: Introductionmentioning

confidence: 99%

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Developing ensemble clustering through similarity measures: A semi‐supervised hierarchical clustering learning

Wang,

2024

Concurrency and Computation

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SummaryA generic variation of hierarchical clustering (HC) that builds clusters from the bottom up is called agglomerative hierarchical clustering (AHC). The extension of AHC techniques using similarity criteria is the main topic of this research. Based on this, we create an AHC method that accomplishes clustering through ensemble approaches and combines the clustering of clusters with an original similarity measure. Three steps make up the proposed method's primary section. The first phase combines several individual AHC techniques to identify links between samples and create preliminary clusters. A heuristic similarity measure based on the developed clusters is used to determine how similar the samples are. The initial clusters produced using various techniques are all re‐clustered to create superclusters in the second step. The third phase involves creating the final clusters by assigning each sample to a supercluster with the greatest similarity after the clusters have been formed. Based on several benchmark datasets from the UCI machine learning repository, extensive experimental research has been done to assess the performance of the suggested approach. The outcomes unequivocally demonstrate that the suggested AHC‐based paradigm outperforms cutting‐edge techniques.

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Section: Introductionmentioning

confidence: 99%

“…1,2 Assigning labels to samples that identify each sample's membership in the cluster is the goal of clustering. 3 The clustering outcomes for the same data can vary because different clustering methods employ varied similarity/distance measuring strategies. Clustering methods are considered unsupervised machine learning techniques.…”

Section: Introductionmentioning

confidence: 99%

Developing ensemble clustering through similarity measures: A semi‐supervised hierarchical clustering learning

Wang,

2024

Concurrency and Computation

View full text Add to dashboard Cite

show abstract

Study of Document Clustering Algorithms Applied on Covid Data

Cited by 1 publication

References 21 publications

Developing ensemble clustering through similarity measures: A semi‐supervised hierarchical clustering learning

Developing ensemble clustering through similarity measures: A semi‐supervised hierarchical clustering learning

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