MapView: Exploring Datasets via Unsupervised View Recommendation

Carvalho TBA, Martins DML, Lima Neto FB

Research article in edited proceedings (conference) | Peer reviewed

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.

Details about the publication

Publisherunknown, unknown;
Book title{IEEE} Latin American Conference on Computational Intelligence, {LA-CCI} 2021, Temuco, Chile, November 2-4, 2021
Page range1-6
Publishing companyWiley-IEEE Press
Place of publicationTemuco, Chile
StatusPublished
Release year2021
Conference2021 IEEE Latin American Conference on Computational Intelligence (LA-CCI), Temuco, Chile
ISBN978-1-7281-8864-5
DOI10.1109/LA-CCI48322.2021.9769785
Link to the full texthttps://doi.org/10.1109/LA-CCI48322.2021.9769785
Keywordsview recommendation; data exploration; self-organizing maps

Authors from the University of Münster

Lima Martins, Denis Mayr
Chair of Machine Learning and Data Engineering (Prof. Gieseke) (MLDE)