MapView: Exploring Datasets via Unsupervised View Recommendation

Carvalho TBA, Martins DML, Lima Neto FB


Abstract

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.

Keywords
view recommendation; data exploration; self-organizing maps



Publication type
Forschungsartikel in Sammelband (Konferenz)

Peer reviewed
Yes

Publication status
Published

Year
2021

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

Venue
Temuco

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

Editor
unknown, unknown;

Start page
1

End page
6

Publisher
IEEE

Place
Temuco, Chile

ISBN
978-1-7281-8864-5

DOI

Full text