Data-driven class discovery, i.e., the inference of cluster structure in a dataset, is a fundamental task in Data Analysis, in particular for the Life Sciences. We provide a tutorial on the most common approaches used for that task, focusing on methodologies for the prediction of the number of clusters in a dataset. Although the methods that we present are general in terms of the data for which they can be used, we offer a case study relevant for Microarray Data Analysis
Giancarlo, R., Utro, F. (2017). Computation Cluster Validation in the Big Data Era. In Reference Module in the Life Sciences [10.1016/B978-0-12-809633-8.20385-3].
Computation Cluster Validation in the Big Data Era
Giancarlo, Raffaele;Utro, Filippo
2017-01-01
Abstract
Data-driven class discovery, i.e., the inference of cluster structure in a dataset, is a fundamental task in Data Analysis, in particular for the Life Sciences. We provide a tutorial on the most common approaches used for that task, focusing on methodologies for the prediction of the number of clusters in a dataset. Although the methods that we present are general in terms of the data for which they can be used, we offer a case study relevant for Microarray Data Analysis
| File | Dimensione | Formato | |
|---|---|---|---|
|
giancarlo2018(1).pdf
Solo gestori archvio
Dimensione
331.98 kB
Formato
Adobe PDF
|
331.98 kB | Adobe PDF | Visualizza/Apri Richiedi una copia |
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
