We propose a tool for exploring the number of clusters based on pivotal methods and consensus clustering. K-means algorithm is used to learn the pairwise similarity via the co-occurrence of points in multiple partitions of the data. This similarity can be used to investigate the number of groups and detect arbitrary shaped clusters. Different criteria for identifying the pivots are discussed, as well as preliminary results concerning the selection of the optimal number of clusters.
Assessing the number of groups in consensus clustering by pivotal methods
Roberta Pappada;Francesco Pauli;Nicola Torelli
2021-01-01
Abstract
We propose a tool for exploring the number of clusters based on pivotal methods and consensus clustering. K-means algorithm is used to learn the pairwise similarity via the co-occurrence of points in multiple partitions of the data. This similarity can be used to investigate the number of groups and detect arbitrary shaped clusters. Different criteria for identifying the pivots are discussed, as well as preliminary results concerning the selection of the optimal number of clusters.File in questo prodotto:
| File | Dimensione | Formato | |
|---|---|---|---|
|
Pappada_Assessing the number of groups in consensus clustering by pivotal methods.pdf
Accesso chiuso
Descrizione: contributo con frontespizio e indice del volume
Tipologia:
Documento in Versione Editoriale
Licenza:
Copyright Editore
Dimensione
405.39 kB
Formato
Adobe PDF
|
405.39 kB | Adobe PDF | Visualizza/Apri Richiedi una copia |
Pubblicazioni consigliate
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
