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A constrained randomization approach to interactive visual data exploration with subjective feedback

Bo Kang (UGent) , Kai Puolamäki, Jefrey Lijffijt (UGent) and Tijl De Bie (UGent)
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Abstract
Data visualization and iterative/interactive data mining are growing rapidly in attention, both in research as well as in industry. However, while there are plethora of advanced data mining methods and lots of works in the field of visualisation, integrated methods that combine advanced visualization and/or interaction with data mining techniques in a principled way are rare. We present a framework based on constrained randomization which lets users explore high-dimensional data via ‘subjectively informative’ two-dimensional data visualizations. The user is presented with ‘interesting’ projections, allowing users to express their observations using visual interactions that update a background model representing the user's belief state. This background model is then considered by a projection-finding algorithm employing data randomization to compute a new ‘interesting’ projection. By providing users with information that contrasts with the background model, we maximize the chance that the user encounters striking new information present in the data. This process can be iterated until the user runs out of time or until the difference between the randomized and the real data is insignificant. We present two case studies, one controlled study on synthetic data and another on census data, using the proof-of-concept tool SIDE that demonstrates the presented framework.
Keywords
Exploratory data mining, dimensionality reduction, data randomization, subjective interestingness, Data visualization, Data models, Computational modeling, Data mining, Reactive power, Visualization, Tools

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Please use this url to cite or link to this publication:

MLA
Kang, Bo, et al. “A Constrained Randomization Approach to Interactive Visual Data Exploration with Subjective Feedback.” IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, 2020.
APA
Kang, B., Puolamäki, K., Lijffijt, J., & De Bie, T. (2020). A constrained randomization approach to interactive visual data exploration with subjective feedback. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING.
Chicago author-date
Kang, Bo, Kai Puolamäki, Jefrey Lijffijt, and Tijl De Bie. 2020. “A Constrained Randomization Approach to Interactive Visual Data Exploration with Subjective Feedback.” IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING.
Chicago author-date (all authors)
Kang, Bo, Kai Puolamäki, Jefrey Lijffijt, and Tijl De Bie. 2020. “A Constrained Randomization Approach to Interactive Visual Data Exploration with Subjective Feedback.” IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING.
Vancouver
1.
Kang B, Puolamäki K, Lijffijt J, De Bie T. A constrained randomization approach to interactive visual data exploration with subjective feedback. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING. 2020;
IEEE
[1]
B. Kang, K. Puolamäki, J. Lijffijt, and T. De Bie, “A constrained randomization approach to interactive visual data exploration with subjective feedback,” IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, 2020.
@article{8612653,
  abstract     = {Data visualization and iterative/interactive data mining are growing rapidly in attention, both in research as well as in industry. However, while there are plethora of advanced data mining methods and lots of works in the field of visualisation, integrated methods that combine advanced visualization and/or interaction with data mining techniques in a principled way are rare. We present a framework based on constrained randomization which lets users explore high-dimensional data via ‘subjectively informative’ two-dimensional data visualizations. The user is presented with ‘interesting’ projections, allowing users to express their observations using visual interactions that update a background model representing the user's belief state. This background model is then considered by a projection-finding algorithm employing data randomization to compute a new ‘interesting’ projection. By providing users with information that contrasts with the background model, we maximize the chance that the user encounters striking new information present in the data. This process can be iterated until the user runs out of time or until the difference between the randomized and the real data is insignificant. We present two case studies, one controlled study on synthetic data and another on census data, using the proof-of-concept tool SIDE that demonstrates the presented framework.},
  author       = {Kang, Bo and Puolamäki, Kai and Lijffijt, Jefrey and De Bie, Tijl},
  issn         = {1041-4347},
  journal      = {IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING},
  keywords     = {Exploratory data mining,dimensionality reduction,data randomization,subjective interestingness,Data visualization,Data models,Computational modeling,Data mining,Reactive power,Visualization,Tools},
  language     = {eng},
  pages        = {14},
  title        = {A constrained randomization approach to interactive visual data exploration with subjective feedback},
  url          = {http://dx.doi.org/10.1109/TKDE.2019.2907082},
  year         = {2020},
}

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