K-means++

Known as: Kmeans++ 
In data mining, k-means++ is an algorithm for choosing the initial values (or "seeds") for the k-means clustering algorithm. It was proposed in 2007… (More)
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2016
2016
The quality of K-Means clustering is extremely sensitive to proper initialization. The classic remedy is to apply k-means++ to… (More)
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2016
2016
Seeding – the task of finding initial cluster centers – is critical in obtaining highquality clusterings for k-Means. However, k… (More)
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2014
2014
k-means is undoubtedly one of the most popular clustering algorithms owing to its simplicity and efficiency. However, this… (More)
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2013
2013
The tremendous growth in data volumes has created a need for new tools and algorithms to quickly analyze large datasets. Cluster… (More)
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Highly Cited
2012
Highly Cited
2012
Over half a century old and showing no signs of aging, k-means remains one of the most popular data processing algorithms. As is… (More)
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2012
2012
The k-means clustering method is a widely used clustering technique for the Web because of its simplicity and speed. However, the… (More)
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2012
2012
k-means++ [5] seeding procedure is a simple sampling based algorithm that is used to quickly find k centers which may then be… (More)
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2012
2012
K-means is undoubtedly the most widely used partitional clustering algorithm. Unfortunately, due to its gradient descent nature… (More)
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Highly Cited
2009
Highly Cited
2009
We provide a clustering algorithm that approximately optimizes the k-means objective, in the one-pass streaming setting. We make… (More)
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Highly Cited
2007
Highly Cited
2007
The k-means method is a widely used clustering technique that seeks to minimize the average squared distance between points in… (More)
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