Abstract
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.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 1-16 |
| Number of pages | 16 |
| Journal | Frontiers of Computer Science |
| DOIs | |
| State | Accepted/In press - Oct 27 2016 |
Fingerprint
Keywords
- data mining
- predictive analysis
- predictive visual analytics
- visual analytics
- visualization
ASJC Scopus subject areas
- Theoretical Computer Science
- Computer Science(all)
Cite this
Recent progress and trends in predictive visual analytics. / Lu, Junhua; Chen, Wei; Ma, Yuxin; Ke, Junming; Li, Zongzhuang; Zhang, Fan; Maciejewski, Ross.
In: Frontiers of Computer Science, 27.10.2016, p. 1-16.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Recent progress and trends in predictive visual analytics
AU - Lu, Junhua
AU - Chen, Wei
AU - Ma, Yuxin
AU - Ke, Junming
AU - Li, Zongzhuang
AU - Zhang, Fan
AU - Maciejewski, Ross
PY - 2016/10/27
Y1 - 2016/10/27
N2 - 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.
AB - 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.
KW - data mining
KW - predictive analysis
KW - predictive visual analytics
KW - visual analytics
KW - visualization
UR - http://www.scopus.com/inward/record.url?scp=84992390980&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84992390980&partnerID=8YFLogxK
U2 - 10.1007/s11704-016-6028-y
DO - 10.1007/s11704-016-6028-y
M3 - Article
SP - 1
EP - 16
JO - Frontiers of Computer Science
JF - Frontiers of Computer Science
SN - 2095-2228
ER -