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Visualizing and Explaining Cluster Patterns: A Framework for the Exploratory Analysis of Large Multidimensional Datasets
University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre. (Skövde Artificial Intelligence Lab (SAIL))
2019 (English)Report (Other academic)
Abstract [en]

Large quantities of data are being collected and analyzed by companies and institutions, with the intention of drawing knowledge and value. Advances in storage, computation, automated analysis and visual and interactive techniques have facilitated this process. It is, however, not always transparent on how these can be brought together for the effective and efficient exploration, monitoring and/or processing of data. That is, knowing which automated techniques and frameworks to use for a given task, how to deploy them and integrate them, how to interpret their results and processes, and how to ease their use through visual and interactive techniques.

In the context of exploratory data analysis, where users approach data without preconceived hypotheses, this thesis proposal argues for the benefit of a framework describing the components, techniques and relations that contribute to the visualization and explanation of cluster patterns in large and multidimensional datasets. That is, a Visual Analytics framework aimed at supporting data scientists in their first steps towards understanding the overall structure of a dataset. The problem area is large and, therefore, the scope is limited to the visualization of cluster patterns in table-like datasets with meaningful attributes.

This thesis proposal motivates the relevance of conducting research for the development of such framework. It formalizes a set of research questions and presents a research plan based on the design science research method. Moreover, it provides a description of preliminary results as well as related background theory and state-of-the-art research.

Place, publisher, year, edition, pages
2019. , p. 48
Keywords [en]
visual analytics, machine learning, dimensionality reduction, clustering, visualization, interaction
National Category
Computer and Information Sciences
Research subject
Skövde Artificial Intelligence Lab (SAIL)
Identifiers
URN: urn:nbn:se:his:diva-16931OAI: oai:DiVA.org:his-16931DiVA, id: diva2:1319423
Note

Thesis proposal, PhD programme, University of Skövde

Available from: 2019-05-31 Created: 2019-05-31 Last updated: 2019-06-03Bibliographically approved

Open Access in DiVA

The full text will be freely available from 2021-03-14 16:52
Available from 2021-03-14 16:52

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Ventocilla, Elio

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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf