Visualization for Machine Learning and Knowledge Extraction
Organized by Cagatay TURKAY, Robert S. LARAMEE, Chaomei CHEN & Andreas HOLZINGER
(Note that this special workshop has different deadlines – see below)
Aim & Scope
The central goal of this workshop is to bring together experts from machine learning (area 2 of the HCI-KDD pipeline) and visualization (area 6 of the HCI-KDD pipeline) – see image. Machine learning and Knowledge extraction is an enormously increasing field with many practical applications. Whilst algorithm development is surely at the center of the whole pipeline, it is the visualization which ultimately makes the results accessible to the end user. Visualization thus can be seen as a mapping from arbitrarily high-dimensional spaces to the lower dimensions and plays a central and critical role in interacting with machine learning algorithms, and particularly with interactive machine learning (iML) with the human-in-the-loop.
Background
The unprecedented increase in the amount, variety and value of data has been significantly transforming the way that scientific research is carried out and businesses operate. Knowledge generated from data drives innovation in almost all application domains, including health, transport, cyber security, manufacturing, digital services, and also scientific domains such as biology, medicine, environmental and physical sciences, the humanities and social sciences to name a few. 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. Visual data science approaches not only support the abstraction and communication of findings, but also enable new forms of investigation and exploration that can lead to novel and better-informed observations and data-driven inferences.
Call for Contributions
This workshop calls for contributions that demonstrate the role and value of visualization for machine learning and knowledge extraction but also within the whole data science process. Methods that exemplify the value of the synergy between visualization and fields such as machine learning, statistics or data mining, and examples of applications where such visual data science approaches deliver new ways of extracting knowledge are highly encouraged. The workshop aims to bring together practitioners and researchers from both academia and industry, and aims to spark discussion and cross-breeding through reflections on cross-domain-driven problems and on the use of diverse techniques from various fields.
Suggested topics for papers include, but are not limited to:
- Novel visual representations and interaction techniques to facilitate human-involved interactive machine learning and knowledge extraction processes
- Methods and tools for the presentation, production, and visual dissemination of analytical findings
- Data management and knowledge representation including scalable data representations
- Applications of visual data science that demonstrate the value of such a hybrid approach in a given application domain
- Methods related to clustering, subspace visualization, dimensionality reduction
- Visual analytics and pattern discovery
- Applications of visual analytics
- Graph and Network Visualization
- Algebraic and Computational Topology and Visualization (cross-connex with MAKE-Topology)
Submission format
Accepted papers, 6-20 pages (even page numbers!) – please consult the general CD-MAKE submission guidelines – will be published in the CD-MAKE Springer Volume of Lecture Notes in Artificial Intelligence (LNAI). Selected papers will be invited to a special issue of the Information Visualization (Sage) journal following the event (separate information will be provided soon).
Journal Information:
Sage Information Visualization, SCI IF (2015) = 0.639
http://journals.sagepub.com/home/ivi
http://dblp.uni-trier.de/db/journals/ivs
Information Visualization is essential reading for researchers and practitioners of information visualization and is of interest to computer scientists and data analysts working on related specialisms. This journal is an international, peer-reviewed journal publishing articles on fundamental research and applications of information visualization. The journal acts as a dedicated forum for the theories, methodologies, techniques and evaluations of information visualization and its applications. The journal is a core vehicle for developing a generic research agenda for the field by identifying and developing the unique and significant aspects of information visualization. Emphasis is placed on interdisciplinary material and on the close connection between theory and practice.
Dates (note that these dates are different THAN the general deadlines)
Submission deadline: June, 12, 2017
Camera-Ready deadline: July, 9, 2017 (hard deadline – no extensions possible)
Technical Committee
Colleages from the main CD-MAKE committee working in area 2 and area 6
https://cd-make.net/committees (additional committee members from those areas will be invited)