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Eliciting Structure in Data

Authors :
Holst, Anders
Bouguelia, Mohamed-Rafik
Görnerup, Olof
Pashami, Sepideh
Al-Shishtawy, Ahmad
Falkman, Göran
Karlsson, Alexander
Said, Alan
Bae, Juhee
Girdzijauskas, Sarunas
Nowaczyk, Sławomir
Soliman, Amira
Holst, Anders
Bouguelia, Mohamed-Rafik
Görnerup, Olof
Pashami, Sepideh
Al-Shishtawy, Ahmad
Falkman, Göran
Karlsson, Alexander
Said, Alan
Bae, Juhee
Girdzijauskas, Sarunas
Nowaczyk, Sławomir
Soliman, Amira
Publication Year :
2019

Abstract

This paper demonstrates how to explore and visualize different types of structure in data, including clusters, anomalies, causal relations, and higher order relations. The methods are developed with the goal of being as automatic as possible and applicable to massive, streaming, and distributed data. Finally, a decentralized learning scheme is discussed, enabling finding structure in the data without collecting the data centrally.<br />BIDAF

Details

Database :
OAIster
Notes :
application/pdf, English
Publication Type :
Electronic Resource
Accession number :
edsoai.on1233448987
Document Type :
Electronic Resource