Results 1  10
of
2,254,314
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
Kmeans++: the advantages of careful seeding
 In Proceedings of the 18th Annual ACMSIAM Symposium on Discrete Algorithms
, 2007
"... The kmeans method is a widely used clustering technique that seeks to minimize the average squared distance between points in the same cluster. Although it offers no accuracy guarantees, its simplicity and speed are very appealing in practice. By augmenting kmeans with a very simple, randomized se ..."
Abstract

Cited by 459 (8 self)
 Add to MetaCart
The kmeans method is a widely used clustering technique that seeks to minimize the average squared distance between points in the same cluster. Although it offers no accuracy guarantees, its simplicity and speed are very appealing in practice. By augmenting kmeans with a very simple, randomized
Refining Initial Points for KMeans Clustering
, 1998
"... Practical approaches to clustering use an iterative procedure (e.g. KMeans, EM) which converges to one of numerous local minima. It is known that these iterative techniques are especially sensitive to initial starting conditions. We present a procedure for computing a refined starting condition fro ..."
Abstract

Cited by 308 (5 self)
 Add to MetaCart
Practical approaches to clustering use an iterative procedure (e.g. KMeans, EM) which converges to one of numerous local minima. It is known that these iterative techniques are especially sensitive to initial starting conditions. We present a procedure for computing a refined starting condition
Mean shift, mode seeking, and clustering
 IEEE Transactions on Pattern Analysis and Machine Intelligence
, 1995
"... AbstractMean shift, a simple iterative procedure that shifts each data point to the average of data points in its neighborhood, is generalized and analyzed in this paper. This generalization makes some kmeans like clustering algorithms its special cases. It is shown that mean shift is a modeseeki ..."
Abstract

Cited by 620 (0 self)
 Add to MetaCart
AbstractMean shift, a simple iterative procedure that shifts each data point to the average of data points in its neighborhood, is generalized and analyzed in this paper. This generalization makes some kmeans like clustering algorithms its special cases. It is shown that mean shift is a mode
An Efficient kMeans Clustering Algorithm: Analysis and Implementation
, 2000
"... Kmeans clustering is a very popular clustering technique, which is used in numerous applications. Given a set of n data points in R d and an integer k, the problem is to determine a set of k points R d , called centers, so as to minimize the mean squared distance from each data point to its ..."
Abstract

Cited by 405 (4 self)
 Add to MetaCart
Kmeans clustering is a very popular clustering technique, which is used in numerous applications. Given a set of n data points in R d and an integer k, the problem is to determine a set of k points R d , called centers, so as to minimize the mean squared distance from each data point to its
Xmeans: Extending Kmeans with Efficient Estimation of the Number of Clusters
 In Proceedings of the 17th International Conf. on Machine Learning
, 2000
"... Despite its popularity for general clustering, Kmeans suffers three major shortcomings; it scales poorly computationally, the number of clusters K has to be supplied by the user, and the search is prone to local minima. We propose solutions for the first two problems, and a partial remedy for the t ..."
Abstract

Cited by 412 (5 self)
 Add to MetaCart
Despite its popularity for general clustering, Kmeans suffers three major shortcomings; it scales poorly computationally, the number of clusters K has to be supplied by the user, and the search is prone to local minima. We propose solutions for the first two problems, and a partial remedy
A comparison of document clustering techniques
 In KDD Workshop on Text Mining
, 2000
"... This paper presents the results of an experimental study of some common document clustering techniques: agglomerative hierarchical clustering and Kmeans. (We used both a “standard” Kmeans algorithm and a “bisecting ” Kmeans algorithm.) Our results indicate that the bisecting Kmeans technique is ..."
Abstract

Cited by 600 (29 self)
 Add to MetaCart
This paper presents the results of an experimental study of some common document clustering techniques: agglomerative hierarchical clustering and Kmeans. (We used both a “standard” Kmeans algorithm and a “bisecting ” Kmeans algorithm.) Our results indicate that the bisecting Kmeans technique
Spectral Relaxation for Kmeans Clustering
, 2001
"... The popular Kmeans clustering partitions a data set by minimizing a sumofsquares cost function. A coordinate descend method is then used to find local minima. In this paper we show that the minimization can be reformulated as a trace maximization problem associated with the Gram matrix of the dat ..."
Abstract

Cited by 197 (27 self)
 Add to MetaCart
The popular Kmeans clustering partitions a data set by minimizing a sumofsquares cost function. A coordinate descend method is then used to find local minima. In this paper we show that the minimization can be reformulated as a trace maximization problem associated with the Gram matrix
Distance Metric Learning, With Application To Clustering With SideInformation
 ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 15
, 2003
"... Many algorithms rely critically on being given a good metric over their inputs. For instance, data can often be clustered in many "plausible" ways, and if a clustering algorithm such as Kmeans initially fails to find one that is meaningful to a user, the only recourse may be for the us ..."
Abstract

Cited by 799 (14 self)
 Add to MetaCart
Many algorithms rely critically on being given a good metric over their inputs. For instance, data can often be clustered in many "plausible" ways, and if a clustering algorithm such as Kmeans initially fails to find one that is meaningful to a user, the only recourse may
Estimating the number of clusters in a dataset via the Gap statistic
, 2000
"... We propose a method (the \Gap statistic") for estimating the number of clusters (groups) in a set of data. The technique uses the output of any clustering algorithm (e.g. kmeans or hierarchical), comparing the change in within cluster dispersion to that expected under an appropriate reference ..."
Abstract

Cited by 492 (1 self)
 Add to MetaCart
We propose a method (the \Gap statistic") for estimating the number of clusters (groups) in a set of data. The technique uses the output of any clustering algorithm (e.g. kmeans or hierarchical), comparing the change in within cluster dispersion to that expected under an appropriate reference
Results 1  10
of
2,254,314