首页|Recent progress and trends in predictive visual analytics

Recent progress and trends in predictive visual analytics

扫码查看
原文链接
  • NETL
  • NSTL
  • 万方数据
  • 维普
A wide variety of predictive analytics techniques have been developed in statistics,machine learning and data mining;however,many of these algorithms take a black-box approach in which data is input and future predictions are output with no insight into what goes on during the process.Unfortunately,such a closed system approach often leaves little room for injecting domain expertise and can result in frustration from analysts when results seem spurious or confusing.In order to allow for more human-centric approaches,the visualization community has begun developing methods to enable users to incorporate expert knowledge into the prediction process at all stages,including data cleaning,feature selection,model building and model validation.This paper surveys current progress and trends in predictive visual analytics,identifies the common framework in which predictive visual analytics systems operate,and develops a summarization of the predictive analytics workflow.

predictive visual analyticsvisualizationvisual analyticsdata miningpredictive analysis

Junhua LU、Wei CHEN、Yuxin MA、Junming KE、Zongzhuang LI、Fan ZHANG、Ross MACIEJEWSKI

展开 >

State Key Lab of Computer Aided Design and Computer Graphics,Zhejiang University,Hangzhou 310058,China

College of Science,Zhejiang University of Technology,Hangzhou 310023,China

College of Computer Science and Technology,Zhejiang University of Technology,Hangzhou 310023,China

School of Computing,Informatics and Decision Systems Engineering,Arizona State University,Tempe AZ 85287-8809,USA

展开 >

This work was supported by National Basic Research Program of China (973 Program)Major Program of the National Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaZhejiang Provincial Natural Science Foundation of ChinaFundamental Research Funds for the Central Universities,the Innovation Joint Research Center for Cyber-Physical-Society Syst

2015CB352503612320126130314161422211u1536118u1536119LR13F0200011350573

2017

10.1007/s11704-016-6028-y

计算机科学前沿
高等教育出版社

计算机科学前沿

CSTPCDCSCDSCIEI
影响因子：0.303
ISSN：2095-2228
年,卷(期)：2017.11(2)
  • 2
  • 1