Constrained clustering

Known as: Chunklet, Must-link 
In computer science, constrained clustering is a class of semi-supervised learning algorithms. Typically, constrained clustering incorporates either… (More)
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Highly Cited
2012
Highly Cited
2012
Constrained clustering has been well-studied for algorithms such as K-means and hierarchical clustering. However, how to satisfy… (More)
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Highly Cited
2010
Highly Cited
2010
Constrained clustering has been well-studied for algorithms like K-means and hierarchical agglomerative clustering. However, how… (More)
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Highly Cited
2008
Highly Cited
2008
New updated! The latest book from a very famous author finally comes out. Book of constrained clustering advances in algorithms… (More)
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Highly Cited
2008
Highly Cited
2008
Pairwise constraints specify whether or not two samples should be in one cluster. Although it has been successful to incorporate… (More)
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Highly Cited
2008
Highly Cited
2008
We describe a method of segmenting musical audio into structural sections based on a hierarchical labeling of spectral features… (More)
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Highly Cited
2006
Highly Cited
2006
For the task of near-duplicated document detection, both traditional fingerprinting techniques used in database community and bag… (More)
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Highly Cited
2004
Highly Cited
2004
Semi-supervised clustering uses a small amount of supervised data to aid unsupervised learning. One typical approach specifies a… (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
1996
Highly Cited
1996
Many video programs have story structures that can be recognized through the clustering of video contents based on low-level… (More)
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Highly Cited
1993
Highly Cited
1993
Our deterministic annealing approach to clustering is derived on the basis of the principle of maximum entropy, is independent of… (More)
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