Results 251 to 260 of about 606,836 (279)

Optimized Data Fusion for Kernel k-Means Clustering

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012
This paper presents a novel optimized kernel k-means algorithm (OKKC) to combine multiple data sources for clustering analysis. The algorithm uses an alternating minimization framework to optimize the cluster membership and kernel coefficients as a nonconvex problem.
Leon-Charles Tranchevent, Xinhai Liu, Wolfgang Glänzel   +2 more
exaly   +3 more sources

The global kernel k-means clustering algorithm

2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence), 2008
Kernel k-means is an extension of the standard k-means clustering algorithm that identifies nonlinearly separable clusters. In order to overcome the cluster initialization problem associated with this method, in this work we propose the global kernel k-means algorithm, a deterministic and incremental approach to kernel-based clustering. Our method adds
Grigorios Tzortzis, Aristidis Likas
openaire   +1 more source

CPU and GPU parallelized kernel K-means

The Journal of Supercomputing, 2018
K-means is one of the most commonly used clustering algorithms, with diverse scope for implementation in the signal processing, artificial intelligence and image processing fields, among others. Different variations and improvements of K-means exist, with kernel K-means being the most famous.
Mohammed Baydoun, Hassan Ghaziri, Mohammed Al-Husseini   +2 more
openaire   +1 more source

The Kernel Rough K-Means Algorithm

Recent Advances in Computer Science and Communications, 2020
Background: Clustering is one of the most important data mining methods. The k-means (c-means ) and its derivative methods are the hotspot in the field of clustering research in recent years. The clustering method can be divided into two categories according to the uncertainty, which are hard clustering and soft clustering. The Hard C-Means clustering
Wang Meng, Dui Hongyan, Zhou Shiyuan, Dong Zhankui, Wu Zige   +4 more
openaire   +1 more source

Kernel K-Means for Categorical Data

2005
Clustering categorical data is an important and challenging data analysis task. In this paper, we explore the use of kernel K-means to cluster categorical data. We propose a new kernel function based on Hamming distance to embed categorical data in a constructed feature space where the clustering is conducted. We experimentally evaluated the quality of
openaire   +1 more source

Privacy-Preserving Kernel k-Means Outsourcing with Randomized Kernels

2013 IEEE 13th International Conference on Data Mining Workshops, 2013
Kernel k-means is a useful way to identify clusters for nonlinearly separable data. Solving the kernel k-means problem is time consuming due to the quadratic computational complexity. Outsourcing the computations of solving kernel k-means to external cloud computing service providers benefits the data owner who has only limited computing resources ...
openaire   +1 more source

A Kernel Iterative K-Means Algorithm

2019
In this paper Mercer kernels with certain invariance properties are briefly introduced and an apparently not well-known construction using certain cohomology groups is described. As a consequence some kernels arising from this are given. Hence a kernel version of an iterative k-means algorithm due to Duda et al. is exhibited.
openaire   +1 more source

Kernel k’-means Algorithm for Clustering Analysis

2013
k'-means algorithm is a new improvement of k-means algorithm. It implements a rewarding and penalizing competitive learning mechanism into the k-means paradigm such that the number of clusters can be automatically determined for a given dataset. This paper further proposes the kernelized versions of k'-means algorithms with four different discrepancy ...
Yue Zhao, Shuyi Zhang, Jinwen Ma
openaire   +1 more source

A kernel k-means clustering algorithm based on an adaptive Mahalanobis kernel

2014 International Joint Conference on Neural Networks (IJCNN), 2014
In this paper, a kernel k-means algorithm based on an adaptive Mahalanobis kernel is proposed. This kernel is built based on an adaptive quadratic distance defined by a symmetric positive definite matrix that changes at each algorithm iteration and takes into account the correlations between variables, allowing the discovery of clusters with non ...
Marcelo Rodrigo Portela Ferreira, Francisco de A. T. de Carvalho   +1 more
openaire   +1 more source

Kernelized K-Means Algorithm Based on Gaussian Kernel

2012
We proposed a kernelized k-means algorithm based on the Gaussian kernel function according to the concepts of support vector clustering and kernel methods. A statistical point of view of robust properties of the proposed method is analyzed. The cluster center estimates obtained by the proposed method can be represented by an M-estimate with a bounded ϕ
Kuo-Lung Wu, You-Jun Lin
openaire   +1 more source