Results 11  20
of
1,847,574
Towards Explaining the Speed of kMeans
"... The kmeans method is a popular algorithm for clustering, known for its speed in practice. This stands in contrast to its exponential worstcase runningtime. To explain the speed of the kmeans method, a smoothed analysis has been conducted. We sketch this smoothed analysis and a generalization to ..."
Abstract
 Add to MetaCart
The kmeans method is a popular algorithm for clustering, known for its speed in practice. This stands in contrast to its exponential worstcase runningtime. To explain the speed of the kmeans method, a smoothed analysis has been conducted. We sketch this smoothed analysis and a generalization
k*Means: A new generalized kmeans clustering algorithm
 Pattern Recognition Letters
"... This paper presents a generalized version of the conventional kmeans clustering algorithm [Proceedings of 5th ..."
Abstract

Cited by 19 (0 self)
 Add to MetaCart
This paper presents a generalized version of the conventional kmeans clustering algorithm [Proceedings of 5th
MultiResolution KMeans Clustering of Time Series and Application to
 Images, Workshop on Multimedia Data Mining, the 4th SIGKDD International Conference on Knowledge Discovery and Data Mining. Washington D.C
, 2003
"... Clustering is vital in the process of condensing and outlining information, since it can provide a synopsis of the stored data. However, the high dimensionality of multimedia data today presents an insurmountable challenge for clustering algorithms. Based on the well known fact that time series and ..."
Abstract

Cited by 1 (1 self)
 Add to MetaCart
Clustering is vital in the process of condensing and outlining information, since it can provide a synopsis of the stored data. However, the high dimensionality of multimedia data today presents an insurmountable challenge for clustering algorithms. Based on the well known fact that time series
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
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
is often difficult, we provide methods for training kmeans using supervised data. Given training data in the form of sets of items with their desired partitioning, we provide a structural SVM method that learns a distance measure so that kmeans produces the desired clusterings1. We propose two variants
Clustering binary data streams with Kmeans
 In Proc. ACM SIGMOD Data Mining and Knowledge Discovery Workshop
, 2003
"... Clustering data streams is an interesting Data Mining problem. This article presents three variants of the Kmeans algorithm to cluster binary data streams. The variants include Online Kmeans, Scalable Kmeans, and Incremental Kmeans, a proposed variant introduced that nds higher quality soluti ..."
Abstract

Cited by 63 (9 self)
 Add to MetaCart
Clustering data streams is an interesting Data Mining problem. This article presents three variants of the Kmeans algorithm to cluster binary data streams. The variants include Online Kmeans, Scalable Kmeans, and Incremental Kmeans, a proposed variant introduced that nds higher quality
Lambertian Reflectance and Linear Subspaces
, 2000
"... We prove that the set of all reflectance functions (the mapping from surface normals to intensities) produced by Lambertian objects under distant, isotropic lighting lies close to a 9D linear subspace. This implies that, in general, the set of images of a convex Lambertian object obtained under a wi ..."
Abstract

Cited by 514 (20 self)
 Add to MetaCart
We prove that the set of all reflectance functions (the mapping from surface normals to intensities) produced by Lambertian objects under distant, isotropic lighting lies close to a 9D linear subspace. This implies that, in general, the set of images of a convex Lambertian object obtained under a
ii Kmean Based Clustering and Context Quantization
"... In this thesis, we study the problems of Kmeans clustering and context quantization. The main task of Kmeans clustering is to partition the training patterns into k distinct groups or clusters that minimize the meansquareerror (MSE) objective function. But the main difficulty of conventional Km ..."
Abstract
 Add to MetaCart
clustering is that its classification performance is highly susceptible to the initialized solution or codebook. Hence the main goal of this research work is to investigate the effective Kmeans clustering algorithms to overcome this difficulty. An extensive task addressed by this thesis is to design a
Discriminative kmeans for clustering
 In Proceedings of the Annual Conference on Advances in Neural Information Processing Systems 21
, 2007
"... We present a theoretical study on the discriminative clustering framework, recently proposed for simultaneous subspace selection via linear discriminant analysis (LDA) and clustering. Empirical results have shown its favorable performance in comparison with several other popular clustering algorithm ..."
Abstract

Cited by 30 (1 self)
 Add to MetaCart
algorithms. However, the inherent relationship between subspace selection and clustering in this framework is not well understood, due to the iterative nature of the algorithm. We show in this paper that this iterative subspace selection and clustering is equivalent to kernel Kmeans with a specific kernel
PrivacyPreserving KMeans Clustering over Vertically Partitioned Data
 IN SIGKDD
, 2003
"... Privacy and security concerns can prevent sharing of data, derailing data mining projects. Distributed knowledge discovery, if done correctly, can alleviate this problem. The key is to obtain valid results, while providing guarantees on the (non)disclosure of data. We present a method for kmeans cl ..."
Abstract

Cited by 159 (9 self)
 Add to MetaCart
Privacy and security concerns can prevent sharing of data, derailing data mining projects. Distributed knowledge discovery, if done correctly, can alleviate this problem. The key is to obtain valid results, while providing guarantees on the (non)disclosure of data. We present a method for kmeans
Results 11  20
of
1,847,574