Högskolan i Skövde

his.sePublikasjoner
Endre søk
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • apa-cv
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf
Evaluating Insight for Interactive Clustering Systems
Högskolan i Skövde, Institutionen för informationsteknologi.
2017 (engelsk)Independent thesis Advanced level (degree of Master (One Year)), 10 poäng / 15 hpOppgave
Abstract [en]

Clustering is a very common technique for finding similar records in a dataset, but classic clustering systems do not allow interaction or steering of the cluster generation process. Interactive clustering, the integration of clustering into visual analytics, on the other hand allows the analyst to interact during the cluster generation process. This can lead to more meaningful insights and can increase the trust and the transparency of the whole clustering process. Evaluations have already shown that these systems can produce clusters with lower error rates. However, there is little work on evaluating these systems with respect to insight generation. In this thesis, a literature study is conducted with the goal to provide guidelines for an insight-based evaluation of interactive clustering systems as well as interaction methods for interactive clustering systems. First, an overview of interactive clustering and different types of interaction is provided. Then, the term insight and three prerequisites (data, users, and tasks) for an evaluation are defined and described. Furthermore, different evaluation procedures are identified and analyzed due to their capability for the evaluation of insight. The results are then used to establish guidelines. They are useful for two reasons: It allows developers to obtain a better understanding of how well a particular system promotes insight generation and it can show which types of interaction between the analyst and the system can lead to high quality insights. In the end, the results are discussed and ideas for future work are provided.

sted, utgiver, år, opplag, sider
2017. , s. 35
HSV kategori
Identifikatorer
URN: urn:nbn:se:his:diva-13620OAI: oai:DiVA.org:his-13620DiVA, id: diva2:1105343
Fag / kurs
Informationsteknologi
Utdanningsprogram
Data Science - Master’s Programme
Presentation
2017-05-24, P502, Kaplansgatan 11, 541 34 Skövde, 11:15 (engelsk)
Veileder
Examiner
Tilgjengelig fra: 2017-06-13 Laget: 2017-06-02 Sist oppdatert: 2018-01-13bibliografisk kontrollert

Open Access i DiVA

Fulltekst mangler i DiVA

Av organisasjonen

Søk utenfor DiVA

GoogleGoogle Scholar

urn-nbn

Altmetric

urn-nbn
Totalt: 431 treff
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • apa-cv
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf