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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

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

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

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

Highly Cited

2004

Highly Cited

2004

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

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

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

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