In most real world scenarios, experts dispose of limited background knowledge that they can exploit for guiding the analysis process. In this context, semi-supervised clustering can be employed to leverage such knowledge and enable the discovery of clusters that meet the analysts’ expectations. To this end, we propose a semi-supervised deep embedding clustering algorithm that exploits triplet constraints as background knowledge within the whole learning process. The latter consists in a two-stage approach where, initially, a low-dimensional data embedding is computed and, successively, cluster assignment is refined via the introduction of an auxiliary target distribution. Our algorithm is evaluated on real-world benchmarks in comparison with state-of-the-art unsupervised and semi-supervised clustering methods. Experimental results highlight the quality of the proposed framework as well as the added value of the new learnt data representation.

Deep Triplet-Driven Semi-supervised Embedding Clustering

Ienco, Dino
Co-first
;
Pensa, Ruggero G.
Co-first
2019

Abstract

In most real world scenarios, experts dispose of limited background knowledge that they can exploit for guiding the analysis process. In this context, semi-supervised clustering can be employed to leverage such knowledge and enable the discovery of clusters that meet the analysts’ expectations. To this end, we propose a semi-supervised deep embedding clustering algorithm that exploits triplet constraints as background knowledge within the whole learning process. The latter consists in a two-stage approach where, initially, a low-dimensional data embedding is computed and, successively, cluster assignment is refined via the introduction of an auxiliary target distribution. Our algorithm is evaluated on real-world benchmarks in comparison with state-of-the-art unsupervised and semi-supervised clustering methods. Experimental results highlight the quality of the proposed framework as well as the added value of the new learnt data representation.
DS2019: 22nd International Conference on Discovery Science
Split, Croatia
October 28-30, 2019
Discovery Science. DS 2019.
Springer
11828
220
234
978-3-030-33777-3
978-3-030-33778-0
https://link.springer.com/chapter/10.1007/978-3-030-33778-0_18
Deep learning, Constrained clustering, Triplet constraints
Ienco, Dino; Pensa, Ruggero G.
File in questo prodotto:
File Dimensione Formato  
ds2019_ienco_printed.pdf

Accesso riservato

Descrizione: PDF online
Tipo di file: PDF EDITORIALE
Dimensione 1.6 MB
Formato Adobe PDF
1.6 MB Adobe PDF   Visualizza/Apri   Richiedi una copia
ds2019_ienco_draft.pdf

Accesso aperto

Descrizione: paper (postprint)
Tipo di file: POSTPRINT (VERSIONE FINALE DELL’AUTORE)
Dimensione 898.59 kB
Formato Adobe PDF
898.59 kB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/2318/1714021
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 2
  • ???jsp.display-item.citation.isi??? ND
social impact