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Highly Cited

2008

Highly Cited

2008

The practice of classifying objects according to perceived similarities is the basis for much of science. Organizing data into… Expand

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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… Expand

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Highly Cited

2004

Highly Cited

2004

The k-means algorithm is well known for its efficiency in clustering large data sets. However, working only on numeric values… Expand

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Highly Cited

2004

Highly Cited

2004

Kernel k-means and spectral clustering have both been used to identify clusters that are non-linearly separable in input space… Expand

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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… Expand

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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… Expand

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Highly Cited

2002

Highly Cited

2002

In k-means clustering, we are given a set of n data points in d-dimensional space R/sup d/ and an integer k and the problem is to… Expand

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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… Expand

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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… Expand

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Highly Cited

1979

Highly Cited

1979

A translation loop with a voltage controlled oscillator (VCO) for generating a plurality of discrete frequencies that are… Expand

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