Fei Wang1, Gui-Fu Lu1

  • 1School of Computer and Information, Anhui Polytechnic University, Wuhu, Anhui, China.

概括

本研究介绍了一种新的多视图集群方法,即可扩展的单通多视图集群与压缩的多尺度二分位图融合 (SOMVC/TMBGF),用于高效的大规模数据分析. SOMVC/TMBGF通过合并多尺度双部分图表和动态加权数据视图来提高集群准确性.

相关概念视频

Collisions in Multiple Dimensions: Introduction01:05

Collisions in Multiple Dimensions: Introduction

It is far more common for collisions to occur in two dimensions; that is, the initial velocity vectors are neither parallel nor antiparallel to each other. Let's see what complications arise from this. The first idea is that momentum is a vector. Like all vectors, it can be expressed as a sum of perpendicular components (usually, though not always, an x-component and a y-component, and a z-component if necessary). Thus, when the statement of conservation of momentum is written for a...
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Collisions in Multiple Dimensions: Problem Solving01:06

Collisions in Multiple Dimensions: Problem Solving

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Scaling01:26

Scaling

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Multicompartment Models: Overview01:14

Multicompartment Models: Overview

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Extraction: Partition and Distribution Coefficients01:14

Extraction: Partition and Distribution Coefficients

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Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

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