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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… Expand
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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
  • J. Huang
  • Data Mining and Knowledge Discovery
  • 2004
  • Corpus ID: 11323096
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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