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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 ..."
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Cited by 459 (8 self)
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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
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 ..."
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Cited by 473 (9 self)
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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
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 ..."
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Cited by 600 (29 self)
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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
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 ..."
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Cited by 308 (5 self)
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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
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 ..."
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Cited by 405 (4 self)
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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 ..."
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Cited by 412 (5 self)
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and their parameters. Experiments show this technique reveals the true number of classes in the underlying distribution, and that it is much faster than repeatedly using accelerated Kmeans for different values of K.
A New Kmean Color Image Segmentation with Cosine Distance for Satellite Images
"... Abstract — This paper represents unsupervised method of kmeans segmentation which is new adaptive technique of colortexture segmentation. With the progress in satellite images, the image segmentation technique for generating and updating geographical information are become more and more important. ..."
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Abstract — This paper represents unsupervised method of kmeans segmentation which is new adaptive technique of colortexture segmentation. With the progress in satellite images, the image segmentation technique for generating and updating geographical information are become more and more important
A Survey of Program Slicing Techniques
 JOURNAL OF PROGRAMMING LANGUAGES
, 1995
"... A program slice consists of the parts of a program that (potentially) affect the values computed at some point of interest, referred to as a slicing criterion. The task of computing program slices is called program slicing. The original definition of a program slice was presented by Weiser in 197 ..."
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Cited by 777 (8 self)
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A program slice consists of the parts of a program that (potentially) affect the values computed at some point of interest, referred to as a slicing criterion. The task of computing program slices is called program slicing. The original definition of a program slice was presented by Weiser in 1979. Since then, various slightly different notions of program slices have been proposed, as well as a number of methods to compute them. An important distinction is that between a static and a dynamic slice. The former notion is computed without making assumptions regarding a program's input, whereas the latter relies on some specific test case. Procedures, arbitrary control flow, composite datatypes and pointers, and interprocess communication each require a specific solution. We classify static and dynamic slicing methods for each of these features, and compare their accuracy and efficiency. Moreover, the possibilities for combining solutions for different features are investigated....
Segmentation of brain MR images through a hidden Markov random field model and the expectationmaximization algorithm
 IEEE TRANSACTIONS ON MEDICAL. IMAGING
, 2001
"... The finite mixture (FM) model is the most commonly used model for statistical segmentation of brain magnetic resonance (MR) images because of its simple mathematical form and the piecewise constant nature of ideal brain MR images. However, being a histogrambased model, the FM has an intrinsic limi ..."
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Cited by 619 (14 self)
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, the HMRFEM framework can easily be combined with other techniques. As an example, we show how the bias field correction algorithm of Guillemaud and Brady (1997) can be incorporated into this framework to achieve a threedimensional fully automated approach for brain MR image segmentation.
SMOTE: Synthetic Minority Oversampling Technique
 Journal of Artificial Intelligence Research
, 2002
"... An approach to the construction of classifiers from imbalanced datasets is described. A dataset is imbalanced if the classification categories are not approximately equally represented. Often realworld data sets are predominately composed of ``normal'' examples with only a small percentag ..."
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Cited by 614 (28 self)
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good means of increasing the sensitivity of a classifier to the minority class. This paper shows that a combination of our method of oversampling the minority (abnormal) class and undersampling the majority (normal) class can achieve better classifier performance (in ROC space) than only under
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