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K-means+++: Outliers-resistant clustering
Adiel Statman
, Liat Rozenberg
,
Dan Feldman
Department of Computer Science
Research output
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peer-review
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Mathematics
Clustering
100%
Minimizes
100%
Approximation Error
100%
M-Estimator
100%
Metric Space
100%
Total Sum
100%
Far Point
100%
K-Means
100%
Center Point
100%
Keyphrases
K-means
100%
Outlier Resistant
100%
K-center
40%
Sum Distance
20%
Sum of Squared Distance
20%
Approximation Error
20%
M-estimator
20%
Metric Space
20%
Farthest Point
20%
Mean Clustering
20%
Non-metric Spaces
20%
Easy-to-implement
20%