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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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Papers overview

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Review
2010
Review
2010
Organizing data into sensible groupings is one of the most fundamental modes of understanding and learning. As an example, a… Expand
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
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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