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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, integrated methods and tools that combine advanced visualization and data mining techniques are rare, and those that exist are often specialized to a single problem or domain. In this paper, we introduce a novel generic method for interactive visual exploration of high-dimensional data. In contrast to most visualization tools, it is not based on the traditional dogma of manually zooming and rotating data. Instead, the tool initially presents the user with an ‘interesting’ projection of the data and then employs data randomization with constraints to allow users to flexibly and intuitively express their interests or beliefs using visual interactions that correspond to exactly defined constraints. These constraints expressed by the user are then taken into account by a projection-finding algorithm to compute a new ‘interesting’ projection, a process that can be iterated until the user runs out of time or finds that constraints explain everything she needs to find from the data. We present the tool by means of two case studies, one controlled study on synthetic data and another on real census data. The data and software related to this paper are available at http://​www.​interesting-patterns.​net/​forsied/​interactive-visual-data-exploration-with-subjective-feedback/​.
Keywords
Dimensionality Reduction, Data Visualisation, Subjective Interestingness, Exploratory Data Mining

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Citation

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

Chicago
Puolamäki, Kai, Bo Kang, Jefrey Lijffijt, and Tijl De Bie. 2016. “Interactive Visual Data Exploration with Subjective Feedback.” In European Conference, ECML PKDD 2016, Riva Del Garda, Italy, September 19-23, 2016, Proceedings, Part II, Lecture Notes in Computer Science, 9852:214–229. Springer International Publishing.
APA
Puolamäki, K., Kang, B., Lijffijt, J., & De Bie, T. (2016). Interactive visual data exploration with subjective feedback. European Conference, ECML PKDD 2016, Riva del Garda, Italy, September 19-23, 2016, Proceedings, Part II, Lecture Notes in Computer Science (Vol. 9852, pp. 214–229). Presented at the The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery (ECML-PKDD  ’16), Springer International Publishing.
Vancouver
1.
Puolamäki K, Kang B, Lijffijt J, De Bie T. Interactive visual data exploration with subjective feedback. European Conference, ECML PKDD 2016, Riva del Garda, Italy, September 19-23, 2016, Proceedings, Part II, Lecture Notes in Computer Science. Springer International Publishing; 2016. p. 214–29.
MLA
Puolamäki, Kai, Bo Kang, Jefrey Lijffijt, et al. “Interactive Visual Data Exploration with Subjective Feedback.” European Conference, ECML PKDD 2016, Riva Del Garda, Italy, September 19-23, 2016, Proceedings, Part II, Lecture Notes in Computer Science. Vol. 9852. Springer International Publishing, 2016. 214–229. Print.
@inproceedings{8069736,
  abstract     = {Data visualization and iterative/interactive data mining are growing rapidly in attention, both in research as well as in industry. However, integrated methods and tools that combine advanced visualization and data mining techniques are rare, and those that exist are often specialized to a single problem or domain. In this paper, we introduce a novel generic method for interactive visual exploration of high-dimensional data. In contrast to most visualization tools, it is not based on the traditional dogma of manually zooming and rotating data. Instead, the tool initially presents the user with an ‘interesting’ projection of the data and then employs data randomization with constraints to allow users to flexibly and intuitively express their interests or beliefs using visual interactions that correspond to exactly defined constraints. These constraints expressed by the user are then taken into account by a projection-finding algorithm to compute a new ‘interesting’ projection, a process that can be iterated until the user runs out of time or finds that constraints explain everything she needs to find from the data. We present the tool by means of two case studies, one controlled study on synthetic data and another on real census data. The data and software related to this paper are available at http://​www.​interesting-patterns.​net/​forsied/​interactive-visual-data-exploration-with-subjective-feedback/​.},
  author       = {Puolamäki, Kai and Kang, Bo and Lijffijt, Jefrey and De Bie, Tijl},
  booktitle    = {European Conference, ECML PKDD 2016, Riva del Garda, Italy, September 19-23, 2016, Proceedings, Part II, Lecture Notes in Computer Science},
  issn         = {0302-9743},
  keywords     = {Dimensionality Reduction,Data Visualisation,Subjective Interestingness,Exploratory Data Mining},
  language     = {eng},
  location     = {Riva del Garda, Italy},
  pages        = {214--229},
  publisher    = {Springer International Publishing},
  title        = {Interactive visual data exploration with subjective feedback},
  url          = {http://dx.doi.org/10.1007/978-3-319-46227-1_14},
  volume       = {9852},
  year         = {2016},
}

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