Clustering view-segmented documents via tensor modeling

Salvatore Romeo, Andrea Tagarelli, Dino Ienco

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

We propose a clustering framework for view-segmented documents, i.e., relatively long documents made up of smaller fragments that can be provided according to a target set of views or aspects. The framework is designed to exploit a view-based document segmentation into a third-order tensor model, whose decomposition result would enable any standard document clustering algorithm to better reflect the multi-faceted nature of the documents. Experimental results on document collections featuring paragraph-based, metadata-based, or user-driven views have shown the significance of the proposed approach, highlighting performance improvement in the document clustering task.

Original languageEnglish
Title of host publicationFoundations of Intelligent Systems - 21st International Symposium, ISMIS 2014, Proceedings
PublisherSpringer Verlag
Pages385-394
Number of pages10
ISBN (Print)9783319083254
DOIs
Publication statusPublished - 1 Jan 2014
Externally publishedYes
Event21st International Symposium on Methodologies for Intelligent Systems, ISMIS 2014 - Roskilde, Denmark
Duration: 25 Jun 201427 Jun 2014

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8502 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other21st International Symposium on Methodologies for Intelligent Systems, ISMIS 2014
CountryDenmark
CityRoskilde
Period25/6/1427/6/14

Fingerprint

Document Clustering
Metadata
Clustering algorithms
Tensors
Tensor
Clustering
Decomposition
Modeling
Clustering Algorithm
Fragment
Segmentation
Decompose
Target
Experimental Results
Framework
Model
Standards

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Romeo, S., Tagarelli, A., & Ienco, D. (2014). Clustering view-segmented documents via tensor modeling. In Foundations of Intelligent Systems - 21st International Symposium, ISMIS 2014, Proceedings (pp. 385-394). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8502 LNAI). Springer Verlag. https://doi.org/10.1007/978-3-319-08326-1_39

Clustering view-segmented documents via tensor modeling. / Romeo, Salvatore; Tagarelli, Andrea; Ienco, Dino.

Foundations of Intelligent Systems - 21st International Symposium, ISMIS 2014, Proceedings. Springer Verlag, 2014. p. 385-394 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8502 LNAI).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Romeo, S, Tagarelli, A & Ienco, D 2014, Clustering view-segmented documents via tensor modeling. in Foundations of Intelligent Systems - 21st International Symposium, ISMIS 2014, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 8502 LNAI, Springer Verlag, pp. 385-394, 21st International Symposium on Methodologies for Intelligent Systems, ISMIS 2014, Roskilde, Denmark, 25/6/14. https://doi.org/10.1007/978-3-319-08326-1_39
Romeo S, Tagarelli A, Ienco D. Clustering view-segmented documents via tensor modeling. In Foundations of Intelligent Systems - 21st International Symposium, ISMIS 2014, Proceedings. Springer Verlag. 2014. p. 385-394. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-08326-1_39
Romeo, Salvatore ; Tagarelli, Andrea ; Ienco, Dino. / Clustering view-segmented documents via tensor modeling. Foundations of Intelligent Systems - 21st International Symposium, ISMIS 2014, Proceedings. Springer Verlag, 2014. pp. 385-394 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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