Large-Scale Automatic K-Means Clustering for Heterogeneous Many-Core Supercomputer

A Large Scale Clustering Scheme for Kernel K-Means

Kernel functions can be viewed as a non-linear transformation that increases the separability of the input data by mapping them to a new high dimensional space. The incorporation of kernel function enables the K-Means algorithm to explore the inherent data pattern in the new space. However, the recent applications of kernel KMeans algorithm are confined to small corpora due to its expensive com...

Distributed Kernel K-Means for Large Scale Clustering

Clustering samples according to an effective metric and/or vector space representation is a challenging unsupervised learning task with a wide spectrum of applications. Among several clustering algorithms, k-means and its kernelized version have still a wide audience because of their conceptual simplicity and efficacy. However, the systematic application of the kernelized version of k-means is ...

k-means for fast and accurate large scale clustering

We propose k-means, a new clustering method which efficiently copes with large numbers of clusters and achieves low energy solutions. k-means builds upon the standard k-means (Lloyd’s algorithm) and combines a new strategy to accelerate the convergence with a new low time complexity divisive initialization. The accelerated convergence is achieved through only looking at kn nearest clusters and ...

Genetic Weighted K-means for Large-Scale Clustering Problems

This paper proposes a genetic weighted K-means algorithm called GWKMA, which is a hybridization of a genetic algorithm (GA) and a weighted K-means algorithm (WKMA). GWKMA encodes each individual by a partitioning table which uniquely determines a clustering, and employs three genetic operators (selection, crossover, mutation) and a WKMA operator. The superiority of the GWKMA over the WKMA and o...

K - Means Clustering for Automatic Image Segmentation