Thresher: determining the number of clusters while removing outliers

Thresher: determining the number of clusters while removing outliers

Abstract Background Cluster analysis is the most common unsupervised method for finding hidden groups in data. Clustering presents two main challenges: (1) finding the optimal number of clusters, and (2) removing “outliers” among the objects being clustered. Few clustering algorithms currently deal...

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Bibliographic Details
Main Authors: Min Wang, Zachary B. Abrams, Steven M. Kornblau, Kevin R. Coombes
Format: Article
Language:English
Published: BMC 2018-01-01
Series:BMC Bioinformatics
Subjects:
Clustering
Number of clusters
von Mises-Fisher mixture model
NbClust
SCOD
Gap statistics
Online Access:http://link.springer.com/article/10.1186/s12859-017-1998-9

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http://link.springer.com/article/10.1186/s12859-017-1998-9

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