K-means clustering

Known as: K-means, K-means clustering algorithm, Kmeans 
k-means clustering is a method of vector quantization, originally from signal processing, that is popular for cluster analysis in data mining. k… (More)
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Highly Cited
2010
Highly Cited
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
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
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
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
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
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
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
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
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
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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