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Multitask Learning for Sparse Failure Prediction Read chapter Read first chapter

2019 | OriginalPaper | Chapter

Co-clustering from Tensor Data

Authors : Rafika Boutalbi, Lazhar Labiod, Mohamed Nadif

Published in: Advances in Knowledge Discovery and Data Mining

Publisher: Springer International Publishing

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Abstract

With the exponential growth of collected data in different fields like recommender system (user, items), text mining (document, term), bioinformatics (individual, gene), co-clustering which is a simultaneous clustering of both dimensions of a data matrix, has become a popular technique. Co-clustering aims to obtain homogeneous blocks leading to an easy simultaneous interpretation of row clusters and column clusters. Many approaches exist, in this paper we rely on the latent block model (LBM) which is flexible allowing to model different types of data matrices. We extend its use to the case of a tensor (3D matrix) data in proposing a Tensor LBM (TLBM) allowing different relations between entities. To show the interest of TLBM, we consider continuous and binary datasets. To estimate the parameters, a variational EM algorithm is developed. Its performances are evaluated on synthetic and real datasets to highlight different possible applications.

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Appendix
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Footnotes
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Metadata
Title
Co-clustering from Tensor Data
Authors
Rafika Boutalbi
Lazhar Labiod
Mohamed Nadif
Copyright Year
2019
Book
Advances in Knowledge Discovery and Data Mining

Print ISBN: 978-3-030-16147-7

Electronic ISBN: 978-3-030-16148-4

Copyright Year: 2019

DOI
https://doi.org/10.1007/978-3-030-16148-4_29

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