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Thresher: Determining the Number of Clusters While Removing Outliers
Min Wang
, Zachary B. Abrams
, Steven M. Kornblau
,
Kevin R. Coombes
Research output
:
Contribution to journal
›
Article
›
peer-review
46
Scopus citations
Overview
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Dive into the research topics of 'Thresher: Determining the Number of Clusters While Removing Outliers'. Together they form a unique fingerprint.
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Computer Science
Clustering Algorithm
100%
Cluster Analysis
100%
Simulation Study
100%
Component Analysis
100%
Monte Carlo Simulation
100%
Principal Components
100%
Unsupervised Method
100%
Detecting Outlier
100%
Keyphrases
Threshing
100%
Number of Clusters
100%
Removing Outliers
100%
Optimal number of Clusters
36%
Detecting Outliers
27%
Breast Cancer Data
18%
Principal Coordinate Analysis (PCoA)
9%
Biological Datasets
9%
Cluster Analysis
9%
Clustering Algorithm
9%
Excellent Sensitivity
9%
Mixture Model
9%
Monte Carlo Simulation Study
9%
Unsupervised Method
9%
Gene Expression Omnibus
9%
Von Mises-Fisher Mixtures
9%
Outlier Filtering
9%
Hidden Groups
9%
Mathematics
Optimal Number
100%
Simulation Study
25%
Monte Carlo
25%
Mixture Model
25%
Cluster Analysis
25%
Wide Variety
25%
Principal Component Analysis
25%
Clustering Algorithm
25%
Detecting Outlier
25%