Results 241 to 250 of about 942,452 (277)

Stability of K-Means Clustering

2007
We consider the stability of k-means 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 characterization of clustering stability in terms of the number of optimal ...
Alexander Rakhlin, Andrea Caponnetto
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Geodesic K-means clustering

2008 19th International Conference on Pattern Recognition, 2008
We introduce a class of geodesic distances and extend the K-means clustering algorithm to employ this distance metric. Empirically, we demonstrate that our geodesic K-means algorithm exhibits several desirable characteristics missing in the classical K-means.
Nima Asgharbeygi, Arian Maleki
openaire   +1 more source

K-means clustering on CGRA

2017 27th International Conference on Field Programmable Logic and Applications (FPL), 2017
In this paper we present a k-means clustering algorithm for the Versat architecture, a small and low power Coarse Grained Reconfigurable Array (CGRA). This algorithm targets ultra low energy devices where using a GPU or FPGA accelerator is out of the question.
João D. Lopes, José T. de Sousa, Horácio C. Neto, Mário P. Véstias   +3 more
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k-Means Clustering

2023
In this chapter, we explore the K-means clustering algorithm, emphasizing an accessible approach by minimizing abstract mathematical theories. We present a concrete numerical example with a small dataset to illustrate how clusters can be formed using the K.means clustering algorithm.
Zhiyuan Wang, Sayed Ameenuddin Irfan, Christopher Teoh, Priyanka Hriday Bhoyar   +3 more
openaire   +2 more sources

Research on k-means Clustering Algorithm: An Improved k-means Clustering Algorithm

2010 Third International Symposium on Intelligent Information Technology and Security Informatics, 2010
Clustering analysis method is one of the main analytical methods in data mining, the method of clustering algorithm will influence the clustering results directly. This paper discusses the standard k-means clustering algorithm and analyzes the shortcomings of standard k-means algorithm, such as the k-means clustering algorithm has to calculate the ...
Na Shi, Xumin Liu, Yong Guan
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Spherical k-Means++ Clustering

2015
k-means clustering (KM) algorithm, also called hard c-means clustering (HCM) algorithm, is a very powerful clustering algorithm [1, 2], but it has a serious problem of strong initial value dependence. To decrease the dependence, Arthur and Vassilvitskii proposed an algorithm of k-means++ clustering (KM++) algorithm on 2007 [3].
Yasunori Endo, Sadaaki Miyamoto
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On the optimality of k-means clustering

2013 IEEE International Workshop on Genomic Signal Processing and Statistics, 2013
Although it is typically accepted that cluster analysis is a subjective activity, without an objective framework it is impossible to understand, let alone guarantee, the predictive capacity of clustering. To address this, recent work utilizes random point process theory to develop a probabilistic theory of clustering.
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On Autonomous k-Means Clustering

2005
Clustering is a basic tool in unsupervised machine learning and data mining. One of the simplest clustering approaches is the iterative k-means algorithm. The quality of k-means clustering suffers from being confined to run with fixed k rather than being able to dynamically alter the value of k.
Tapio Elomaa, Heidi Koivistoinen
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Discriminative K-means for Clustering.

2008
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 algorithms. However, the inherent relationship between subspace
Ye, J., Zhao, Z., Wu, M.
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Random Projection for k-means Clustering

2018
We study how much the k-means can be improved if initialized by random projections. The first variant takes two random data points and projects the points to the axis defined by these two points. The second one uses furthest point heuristic for the second point.
Fränti Pasi, Sieranoja Sami
openaire   +1 more source