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K-medoids

Known as: K-medoid, PAM, Partitioning Around Medoids 
The k-medoids algorithm is a clustering algorithm related to the k-means algorithm and the medoidshift algorithm. Both the k-means and k-medoids… Expand
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Papers overview

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Highly Cited
2016
Highly Cited
2016
Clustering plays a very vital role in exploring data, creating predictions and to overcome the anomalies in the data. Clusters… Expand
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2014
2014
Face recognition is one of the most unobtrusive biometric techniques that can be used for access control as well as surveillance… Expand
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Highly Cited
2011
Highly Cited
2011
Clustering is one of the most important research areas in the field of data mining. In simple words, clustering is a division of… Expand
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Highly Cited
2010
Highly Cited
2010
Problem statement: Clustering is one of the most important research ar eas in the field of data mining. Clustering means creating… Expand
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Highly Cited
2009
Highly Cited
2009
This paper proposes a new algorithm for K-medoids clustering which runs like the K-means algorithm and tests several methods for… Expand
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2008
2008
Uncertain data are usually represented in terms of an uncertainty region over which a probability density function (pdf) is… Expand
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2006
2006
In this paper, we propose a video summarization algorithm by multiple extractions of key frames in each shot. This algorithm is… Expand
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2006
2006
We propose a hybrid genetic algorithm for k-medoids clustering. A novel heuristic operator is designed and integrated with the… Expand
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Highly Cited
2003
Highly Cited
2003
Kaufman and Rousseeuw (1990) proposed a clustering algorithm Partitioning Around Medoids (PAM) which maps a distance matrix into… Expand
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Highly Cited
1999
Highly Cited
1999
This paper presents new algorithms (fuzzy e-methods (FCMdd) and fuzzy c trimmed medoids (FCTMdd)) for fuzzy clustering of… Expand
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