Interactive visual data exploration with subjective feedback: An information-theoretic approach

Kai Puolamaki, Emilia Oikarinen, Bo Kang, Jefrey Lijffijt, Tijl De Bie

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

6 Citations (Scopus)


The exploration of high-dimensional real-valued data is one of the fundamental exploratory data analysis (EDA) tasks. Existing methods use predefined criteria for the representation of data. There is a lack of methods eliciting the user's knowledge from the data and showing patterns the user does not know yet. We provide a theoretical model where the user can input the patterns she has learned as knowledge. The background knowledge is used to find a MaxEnt distribution of the data, and the user is shown maximally informative projections in which the MaxEnt distribution and the data differ the most. We provide an interactive open source EDA system, study its performance, and present use cases on real data.

Original languageEnglish
Title of host publicationProceedings of the 34th IEEE International Conference on Data Engineering (ICDE 2018)
Number of pages4
ISBN (Electronic)9781538655207
Publication statusPublished - 24 Oct 2018
MoE publication typeA4 Conference publication
EventInternational Conference on Data Engineering - Paris, France
Duration: 16 Apr 201819 Apr 2018
Conference number: 34


ConferenceInternational Conference on Data Engineering
Abbreviated titleICDE


  • Dimensionality reduction
  • Exploratory data analysis
  • Information theory
  • Subjective interestingness


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