A GPU-accelerated parallel K-means algorithm

TL;DR: An overview of the latest parallel clustering algorithms categorized according to the computing platforms used to handle the Big Data, namely, the horizontal and vertical scaling platforms.

TL;DR: A parallel K-means clustering algorithm based on OpenMP, CUDA and OpenCL paradigms that will generate an unsupervised segmentation map that, combined with a supervised classification map, will offer guidance to the neurosurgeon during the tumor resection task.

TL;DR: In this article, the authors investigate how to implement clustering algorithms on parallel and low-energy devices for edge computing environments, and they present the experiments related to two devices with different features: the quad-core UDOO X86 Advanced+ board and the GPU-based NVIDIA Jetson Nano board.

TL;DR: This paper proposes a method to dynamically define the value of K by optimizing a suitable quality index with special care to the computational cost and proposes a strategy for parallel implementation on modern multicore CPUs.

TL;DR: A system of cluster analysis for genome-wide expression data from DNA microarray hybridization is described that uses standard statistical algorithms to arrange genes according to similarity in pattern of gene expression, finding in the budding yeast Saccharomyces cerevisiae that clustering gene expression data groups together efficiently genes of known similar function.

TL;DR: A simple spectral clustering algorithm that can be implemented using a few lines of Matlab is presented, and tools from matrix perturbation theory are used to analyze the algorithm, and give conditions under which it can be expected to do well.

Abstract: The proceedings of the 2001 Neural Information Processing Systems (NIPS) Conference. The annual conference on Neural Information Processing Systems (NIPS) is the flagship conference on neural computation. The conference is interdisciplinary, with contributions in algorithms, learning theory, cognitive science, neuroscience, vision, speech and signal processing, reinforcement learning and control, implementations, and diverse applications. Only about 30 percent of the papers submitted are accepted for presentation at NIPS, so the quality is exceptionally high. These proceedings contain all of the papers that were presented at the 2001 conference. Bradford Books imprint

TL;DR: Genesis integrates various tools for microarray data analysis such as filters, normalization and visualization tools, distance measures as well as common clustering algorithms including hierarchical clustering, self-organizing maps, k-means, principal component analysis, and support vector machines.