Results 1  10
of
759,738
The hardness of kmeans clustering
, 2008
"... We show that kmeans clustering is an NPhard optimization problem, even if k is fixed to 2. 1 ..."
Abstract

Cited by 26 (0 self)
 Add to MetaCart
We show that kmeans clustering is an NPhard optimization problem, even if k is fixed to 2. 1
Constrained KMeans Clustering
 MSRTR200065, Microsoft Research
, 2000
"... We consider practical methods for adding constraints to the KMeans clustering algorithm in order to avoid local solutions with empty clusters or clusters having very few points. We often observe this phenomena when applying KMeans to datasets where the number of dimensions is n 10 and the number ..."
Abstract

Cited by 39 (0 self)
 Add to MetaCart
We consider practical methods for adding constraints to the KMeans clustering algorithm in order to avoid local solutions with empty clusters or clusters having very few points. We often observe this phenomena when applying KMeans to datasets where the number of dimensions is n 10 and the number
kMeans Clustering
"... Kmeans clustering technique works as a greedy algorithm for partition the nsamples into kclusters so as to reduce the sum of the squared distances to the centroids. A very familiar task in data analysis is that of grouping a set of objects into subsets such that all elements within a group are mo ..."
Abstract
 Add to MetaCart
Kmeans clustering technique works as a greedy algorithm for partition the nsamples into kclusters so as to reduce the sum of the squared distances to the centroids. A very familiar task in data analysis is that of grouping a set of objects into subsets such that all elements within a group
Stability of kmeans clustering
 In COLT
, 2007
"... Abstract. We consider the stability of kmeans clustering problems. Clustering stability is a common heuristics used to determine the number of clusters in a wide variety of clustering applications. We continue the theoretical analysis of clustering stability by establishing a complete characterizat ..."
Abstract

Cited by 26 (1 self)
 Add to MetaCart
Abstract. We consider the stability of kmeans clustering problems. Clustering stability is a common heuristics used to determine the number of clusters in a wide variety of clustering applications. We continue the theoretical analysis of clustering stability by establishing a complete
Spherical kMeans Clustering
 Journal of Statistical Software
, 2012
"... Clustering text documents is a fundamental task in modern data analysis, requiring approaches which perform well both in terms of solution quality and computational efficiency. Spherical kmeans clustering is one approach to address both issues, employing cosine dissimilarities to perform prototype ..."
Abstract

Cited by 3 (1 self)
 Add to MetaCart
Clustering text documents is a fundamental task in modern data analysis, requiring approaches which perform well both in terms of solution quality and computational efficiency. Spherical kmeans clustering is one approach to address both issues, employing cosine dissimilarities to perform
Geodesic Kmeans Clustering
"... We introduce a class of geodesic distances and extend the Kmeans clustering algorithm to employ this distance metric. Empirically, we demonstrate that our geodesic Kmeans algorithm exhibits several desirable characteristics missing in the classical Kmeans. These include adjusting to varying densi ..."
Abstract

Cited by 3 (0 self)
 Add to MetaCart
We introduce a class of geodesic distances and extend the Kmeans clustering algorithm to employ this distance metric. Empirically, we demonstrate that our geodesic Kmeans algorithm exhibits several desirable characteristics missing in the classical Kmeans. These include adjusting to varying
10 kMeans Clustering
"... Probably the most famous clustering formulation is kmeans. This is the focus today. Note: kmeans is not an algorithm, it is a problem formulation. kMeans is in the family of assignment based clustering. Each cluster is represented by a single point, to which all other points in the cluster are “a ..."
Abstract
 Add to MetaCart
Probably the most famous clustering formulation is kmeans. This is the focus today. Note: kmeans is not an algorithm, it is a problem formulation. kMeans is in the family of assignment based clustering. Each cluster is represented by a single point, to which all other points in the cluster
Supervised kMeans Clustering
"... The kmeans clustering algorithm is one of the most widely used, effective, and best understood clustering methods. However, successful use of kmeans requires a carefully chosen distance measure that reflects the properties of the clustering task. Since designing this distance measure by hand is of ..."
Abstract

Cited by 3 (0 self)
 Add to MetaCart
The kmeans clustering algorithm is one of the most widely used, effective, and best understood clustering methods. However, successful use of kmeans requires a carefully chosen distance measure that reflects the properties of the clustering task. Since designing this distance measure by hand
Supervised kMeans Clustering
"... The kmeans clustering algorithm is one of the most widely used, effective, and best understood clustering methods. However, successful use of kmeans requires a carefully chosen distance measure that reflects the properties of the clustering task. Since designing this distance measure by hand is ..."
Abstract
 Add to MetaCart
The kmeans clustering algorithm is one of the most widely used, effective, and best understood clustering methods. However, successful use of kmeans requires a carefully chosen distance measure that reflects the properties of the clustering task. Since designing this distance measure by hand
Constrained Kmeans Clustering with Background Knowledge
 In ICML
, 2001
"... Clustering is traditionally viewed as an unsupervised method for data analysis. However, in some cases information about the problem domain is available in addition to the data instances themselves. In this paper, we demonstrate how the popular kmeans clustering algorithm can be pro tably modi ed ..."
Abstract

Cited by 473 (9 self)
 Add to MetaCart
Clustering is traditionally viewed as an unsupervised method for data analysis. However, in some cases information about the problem domain is available in addition to the data instances themselves. In this paper, we demonstrate how the popular kmeans clustering algorithm can be pro tably modi ed
Results 1  10
of
759,738