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Human-controlled iterative subclustering analysis
Södertörn University, School of Natural Sciences, Technology and Environmental Studies, Media Technology.ORCID iD: 0000-0002-2531-0860
Hanken School of Economics, Helsinki, Finland.
2017 (English)In: Proceedings IEEE BigData 2017, Boston, MA: IEEE, 2017, p. 4754-4756Conference paper, Published paper (Refereed)
Abstract [en]

The accumulation and use of data are rapidly expanding. With it, new kinds of interactions emerge that go beyond traditional data analytics, to the point at which a whole new research area of human-data interaction is has been suggested. Our study reconsiders cluster analysis from this point of view. We aim to redesign the process to be more interactive and transparent for purposes beyond conventional data analysis. We address the core issue of cluster analysis, namely what criteria are to determine the homogeneity of a cluster by means of breaking the algorithm into a sequence of explorative subdivisions proceeding as a human-data dialogue. The system provides the human agent with a heuristic. It is formed by sorting the variables of the data set by descending orthogonality against the variable that was applied as the subdivision criterion of the previous iteration. This allows minimizing redundancy of the analysis while securing distinctions relevant for the analytic intention and contextuality, which go beyond the reach of algorithmic decision. The proposed method constitutes a quick and intuitive access to data mining, facilitating new insights and identifying actionable generalizations.

Place, publisher, year, edition, pages
Boston, MA: IEEE, 2017. p. 4754-4756
Keywords [en]
iterative; sub-clustering; clustering; human-data interaction; exploratory data-mining, big data; data-based decision making
National Category
Media and Communication Technology
Identifiers
URN: urn:nbn:se:sh:diva-34176DOI: 10.1109/BigData.2017.8258532Scopus ID: 2-s2.0-85047737316ISBN: 978-1-5386-2715-0 (electronic)OAI: oai:DiVA.org:sh-34176DiVA, id: diva2:1174404
Conference
IEEE Big Data 2017, Bosten, MA, USA, December 11-14, 2017.
Available from: 2018-01-15 Created: 2018-01-15 Last updated: 2018-06-20Bibliographically approved

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Kaipainen, Mauri

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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • 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