MapView: Exploring Datasets via Unsupervised View Recommendation

Carvalho TBA, Martins DML, Lima Neto FB


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.

Schlüsselwörter
view recommendation; data exploration; self-organizing maps



Publikationstyp
Forschungsartikel in Sammelband (Konferenz)

Begutachtet
Ja

Publikationsstatus
Veröffentlicht

Jahr
2021

Konferenz
2021 IEEE Latin American Conference on Computational Intelligence (LA-CCI)

Konferenzort
Temuco

Buchtitel
{IEEE} Latin American Conference on Computational Intelligence, {LA-CCI} 2021, Temuco, Chile, November 2-4, 2021

Herausgeber
unknown, unknown;

Erste Seite
1

Letzte Seite
6

Verlag
IEEE

Ort
Temuco, Chile

ISBN
978-1-7281-8864-5

DOI

Gesamter Text