MapView: Exploring Datasets via Unsupervised View Recommendation

Carvalho TBA, Martins DML, Lima Neto FB

Forschungsartikel in Sammelband (Konferenz) | Peer reviewed

Zusammenfassung

Exploring large datasets in search for valuable insights requires time and sufficient technical knowledge. In order to alleviate this task, we propose and implemented a prototype of a data exploration tool. It is based on Self-Organizing Maps (SOM) and helps non-technical users with limited technical expertise and time. Our proposed approach employs SOM as a clustering mechanism to group and recommend exploratory data views to the user. This recommendation process can also be personalized to meet user’s intention in an interactive manner. Experimental results show that the reported prototype is effective in recommending valuable views, hence, being of aid in data exploration tasks.

Details zur Publikation

Herausgeber*innenunknown, unknown;
Buchtitel{IEEE} Latin American Conference on Computational Intelligence, {LA-CCI} 2021, Temuco, Chile, November 2-4, 2021
Seitenbereich1-6
VerlagWiley-IEEE Press
ErscheinungsortTemuco, Chile
StatusVeröffentlicht
Veröffentlichungsjahr2021
Konferenz2021 IEEE Latin American Conference on Computational Intelligence (LA-CCI), Temuco, Chile
ISBN978-1-7281-8864-5
DOI10.1109/LA-CCI48322.2021.9769785
Link zum Volltexthttps://doi.org/10.1109/LA-CCI48322.2021.9769785
Stichwörterview recommendation; data exploration; self-organizing maps

Autor*innen der Universität Münster

Lima Martins, Denis Mayr
Lehrstuhl für Maschinelles Lernen und Data Engineering (Prof. Gieseke) (MLDE)