# K-means++

## Papers overview

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2016

2016

- AAAI
- 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

- NIPS
- 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

- IEEE Trans. Parallel Distrib. Syst.
- 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

- IEEE 5th International Conference on Cloud…
- 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

- PVLDB
- 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

- 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

- APPROX-RANDOM
- 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

- IJPRAI
- 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

- NIPS
- 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

- SODA
- 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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