short-paper

Regularized Dual-PPMI Co-clustering for Text Data

Authors:

Séverine Affeldt,

Mohamed NadifAuthors Info & Claims

SIGIR '21: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval

Pages 2263 - 2267

https://doi.org/10.1145/3404835.3463065

Published: 11 July 2021 Publication History

Abstract

Co-clustering of document-term matrices has proved to be more effective than one-sided clustering. By their nature, text data are also generally unbalanced and directional. Recently, the von Mises-Fisher (vMF) mixture model was proposed to handle unbalanced data while harnessing the directional nature of text. In this paper we propose a novel co-clustering approach based on a matrix formulation of vMF model-based co-clustering. This formulation leads to a flexible method for text co-clustering that can easily incorporate both word-word semantic relationships and document-document similarities. By contrast with existing methods, which generally use an additive incorporation of similarities, we propose a dual multiplicative regularization that better encapsulates the underlying text data structure. Extensive evaluations on various real-world text datasets demonstrate the superior performance of our proposed approach over baseline and competitive methods, both in terms of clustering results and co-cluster topic coherence.

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Cited By

Fettal CLabiod LNadif M(2023)Boosting Subspace Co-Clustering via Bilateral Graph ConvolutionIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.330081436:3(960-971)Online publication date: 3-Aug-2023
https://dl.acm.org/doi/10.1109/TKDE.2023.3300814
Zhong YWang HYang WWang LLi T(2023)Multi-objective genetic model for co-clustering ensembleApplied Soft Computing10.1016/j.asoc.2023.110058135:COnline publication date: 1-Mar-2023
https://dl.acm.org/doi/10.1016/j.asoc.2023.110058
Battaglia EPeiretti FPensa R(2023)Fast parameterless prototype-based co-clusteringMachine Language10.1007/s10994-023-06474-y113:4(2153-2181)Online publication date: 21-Nov-2023
https://dl.acm.org/doi/10.1007/s10994-023-06474-y
Show More Cited By

Index Terms

Regularized Dual-PPMI Co-clustering for Text Data
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Clustering and classification

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Published In

cover image ACM Conferences

SIGIR '21: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 2021

2998 pages

ISBN:9781450380379

DOI:10.1145/3404835

General Chairs:
Fernando Diaz
(Google)
,
Chirag Shah
University of Washington
,
Torsten Suel
New York University
,
Program Chairs:
Pablo Castells
Universidad Autónoma de Madrid, Amazon
,
Rosie Jones
Spotify
,
Tetsuya Sakai
Waseda University

Copyright © 2021 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGIR: ACM Special Interest Group on Information Retrieval

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 11 July 2021

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French National Research Agency (ANR)

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SIGIR '21

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SIGIR

SIGIR '21: The 44th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 11 - 15, 2021

Virtual Event, Canada

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Overall Acceptance Rate 792 of 3,983 submissions, 20%

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Cited By

Fettal CLabiod LNadif M(2023)Boosting Subspace Co-Clustering via Bilateral Graph ConvolutionIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.330081436:3(960-971)Online publication date: 3-Aug-2023
https://dl.acm.org/doi/10.1109/TKDE.2023.3300814
Zhong YWang HYang WWang LLi T(2023)Multi-objective genetic model for co-clustering ensembleApplied Soft Computing10.1016/j.asoc.2023.110058135:COnline publication date: 1-Mar-2023
https://dl.acm.org/doi/10.1016/j.asoc.2023.110058
Battaglia EPeiretti FPensa R(2023)Fast parameterless prototype-based co-clusteringMachine Language10.1007/s10994-023-06474-y113:4(2153-2181)Online publication date: 21-Nov-2023
https://dl.acm.org/doi/10.1007/s10994-023-06474-y
Fettal CLabiod LNadif MAl Hasan MXiong L(2022)Subspace Co-clustering with Two-Way Graph ConvolutionProceedings of the 31st ACM International Conference on Information & Knowledge Management10.1145/3511808.3557706(3938-3942)Online publication date: 17-Oct-2022
https://dl.acm.org/doi/10.1145/3511808.3557706

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