On the Challenges and Opportunities in Visualization for Machine Learning and Knowledge Extraction: A Research Agenda

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10410)


We describe a selection of challenges at the intersection of machine learning and data visualization and outline a subjective research agenda based on professional and personal experience. The unprecedented increase in the amount, variety and the value of data has been significantly transforming the way that scientific research is carried out and businesses operate. Within data science, which has emerged as a practice to enable this data-intensive innovation by gathering together and advancing the knowledge from fields such as statistics, machine learning, knowledge extraction, data management, and visualization, visualization plays a unique and maybe the ultimate role as an approach to facilitate the human and computer cooperation, and to particularly enable the analysis of diverse and heterogeneous data using complex computational methods where algorithmic results are challenging to interpret and operationalize. Whilst algorithm development is surely at the center of the whole pipeline in disciplines such as Machine Learning and Knowledge Discovery, it is visualization which ultimately makes the results accessible to the end user. Visualization thus can be seen as a mapping from arbitrarily high-dimensional abstract spaces to the lower dimensions and plays a central and critical role in interacting with machine learning algorithms, and particularly in interactive machine learning (iML) with including the human-in-the-loop. The central goal of the CD-MAKE VIS workshop is to spark discussions at this intersection of visualization, machine learning and knowledge discovery and bring together experts from these disciplines. This paper discusses a perspective on the challenges and opportunities in this integration of these discipline and presents a number of directions and strategies for further research.


Visualization Machine learning Knowledge extraction 


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Copyright information

© IFIP International Federation for Information Processing 2017

Authors and Affiliations

  1. 1.GiCentre, Department of Computer ScienceCity, University of LondonLondonUK
  2. 2.Department of Computer ScienceUniversity of SwanseaSwanseaUK
  3. 3.Holzinger Group, HCI-KDD, Institute for Medical Informatics/StatisticsMedical University GrazGrazAustria

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