# K-means clustering

## Papers overview

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Highly Cited

2010

Highly Cited

2010

- WWW
- 2010

We present two modifications to the popular k-means clustering algorithm to address the extreme requirements for latency… (More)

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Highly Cited

2009

Highly Cited

2009

- CloudCom
- 2009

Data clustering has been received considerable attention in many applications, such as data mining, document retrieval, image… (More)

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Highly Cited

2008

Highly Cited

2008

- Pattern Recognition Letters
- 2008

This paper introduces k0-means algorithm that performs correct clustering without pre-assigning the exact number of clusters… (More)

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Highly Cited

2004

Highly Cited

2004

- ICML
- 2004

Principal component analysis (PCA) is a widely used statistical technique for unsupervised dimension reduction. K-means… (More)

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Highly Cited

2003

Highly Cited

2003

- Pattern Recognition
- 2003

We present the global k-means algorithm which is an incremental approach to clustering that dynamically adds one cluster center… (More)

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Highly Cited

2002

Highly Cited

2002

- IEEE Trans. Pattern Anal. Mach. Intell.
- 2002

ÐIn k-means clustering, we are given a set of n data points in d-dimensional space R and an integer k and the problem is to… (More)

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Highly Cited

2001

Highly Cited

2001

- NIPS
- 2001

The popular K-means clustering partitions a data set by minimizing a sum-of-squares cost function. A coordinate descend method is… (More)

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Highly Cited

2001

Highly Cited

2001

- ICML
- 2001

Clustering is traditionally viewed as an unsupervised method for data analysis. However, in some cases information about the… (More)

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Highly Cited

2000

Highly Cited

2000

- 2000

We consider practical methods for adding constraints to the K-Means clustering algorithm in order to avoid local solutions with… (More)

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Highly Cited

1998

Highly Cited

1998

- ICML
- 1998

Practical approaches to clustering use an iterative procedure (e.g. K-Means, EM) which converges to one of numerous local minima… (More)

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