Article

Document clustering by concept factorization

Authors:

Yihong GongAuthors Info & Claims

SIGIR '04: Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval

Pages 202 - 209

https://doi.org/10.1145/1008992.1009029

Published: 25 July 2004 Publication History

Abstract

In this paper, we propose a new data clustering method called concept factorization that models each concept as a linear combination of the data points, and each data point as a linear combination of the concepts. With this model, the data clustering task is accomplished by computing the two sets of linear coefficients, and this linear coefficients computation is carried out by finding the non-negative solution that minimizes the reconstruction error of the data points. The cluster label of each data point can be easily derived from the obtained linear coefficients. This method differs from the method of clustering based on non-negative matrix factorization (NMF) \citeXu03 in that it can be applied to data containing negative values and the method can be implemented in the kernel space. Our experimental results show that the proposed data clustering method and its variations performs best among 11 algorithms and their variations that we have evaluated on both TDT2 and Reuters-21578 corpus. In addition to its good performance, the new method also has the merit in its easy and reliable derivation of the clustering results.

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Cited By

Xu XHe P(2024)Manifold Peaks Nonnegative Matrix FactorizationIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2022.321292235:5(6850-6862)Online publication date: May-2024
https://doi.org/10.1109/TNNLS.2022.3212922
Li NLeng CCheng IBasu AJiao L(2024)Dual-Graph Global and Local Concept Factorization for Data ClusteringIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2022.317743335:1(803-816)Online publication date: Jan-2024
https://doi.org/10.1109/TNNLS.2022.3177433
Chen MWang CHuang DLai JYu P(2024)Concept Factorization Based Multiview Clustering for Large-Scale DataIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.339220936:11(5784-5796)Online publication date: Nov-2024
https://doi.org/10.1109/TKDE.2024.3392209
Show More Cited By

Index Terms

Document clustering by concept factorization
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Clustering and classification
  2. Information systems applications
    1. Data mining
      1. Clustering

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cover image ACM Conferences

SIGIR '04: Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval

July 2004

624 pages

ISBN:1581138814

DOI:10.1145/1008992

General Chair:
Mark Sanderson
University of Sheffield (UK)
,
Program Chairs:
Kalervo Järvelin
University of Tampere (Finland)
,
James Allan
University of Massachusetts (USA)
,
Peter Bruza
Distributed Systems Technology Centre (Australia)

Copyright © 2004 ACM.

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Publication History

Published: 25 July 2004

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SIGIR04: The 27th ACM/SIGIR International Symposium on Information Retrieval 2004

July 25 - 29, 2004

Sheffield, United Kingdom

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

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Cited By

Xu XHe P(2024)Manifold Peaks Nonnegative Matrix FactorizationIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2022.321292235:5(6850-6862)Online publication date: May-2024
https://doi.org/10.1109/TNNLS.2022.3212922
Li NLeng CCheng IBasu AJiao L(2024)Dual-Graph Global and Local Concept Factorization for Data ClusteringIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2022.317743335:1(803-816)Online publication date: Jan-2024
https://doi.org/10.1109/TNNLS.2022.3177433
Chen MWang CHuang DLai JYu P(2024)Concept Factorization Based Multiview Clustering for Large-Scale DataIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.339220936:11(5784-5796)Online publication date: Nov-2024
https://doi.org/10.1109/TKDE.2024.3392209
Zhao ZYang HDeng ZZhang WLuo CWang GGe YHu S(2024)Pseudolabel Enhanced Multiview Deep Concept Factorization Fuzzy ClusteringIEEE Transactions on Fuzzy Systems10.1109/TFUZZ.2024.341653732:9(5334-5347)Online publication date: Sep-2024
https://doi.org/10.1109/TFUZZ.2024.3416537
Peng CZhang PChen YKang ZChen CCheng Q(2024)Fine-Grained Bipartite Concept Factorization for Clustering2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)10.1109/CVPR52733.2024.02481(26254-26264)Online publication date: 16-Jun-2024
https://doi.org/10.1109/CVPR52733.2024.02481
Kong JShang RZhang WWang CXu S(2024)Robust feature selection via central point link information and sparse latent representationPattern Recognition10.1016/j.patcog.2024.110617154:COnline publication date: 1-Oct-2024
https://dl.acm.org/doi/10.1016/j.patcog.2024.110617
Yang BWu JZhou YZhang XLin ZNie FChen B(2024)Robust spectral embedded bilateral orthogonal concept factorization for clusteringPattern Recognition10.1016/j.patcog.2024.110308150(110308)Online publication date: Jun-2024
https://doi.org/10.1016/j.patcog.2024.110308
Wu JYang BXue ZZhang XLin ZChen B(2024)Fast multi-view clustering via correntropy-based orthogonal concept factorizationNeural Networks10.1016/j.neunet.2024.106170(106170)Online publication date: Feb-2024
https://doi.org/10.1016/j.neunet.2024.106170
Li HZhou YZhao PWang LYu C(2024)Class-driven nonnegative matrix factorization with manifold regularization for data clusteringNeurocomputing10.1016/j.neucom.2024.127751592(127751)Online publication date: Aug-2024
https://doi.org/10.1016/j.neucom.2024.127751
Yang BXue ZWu JZhang XNie FChen B(2024)Anchor-graph regularized orthogonal concept factorization for document clusteringNeurocomputing10.1016/j.neucom.2023.127173573(127173)Online publication date: Mar-2024
https://doi.org/10.1016/j.neucom.2023.127173
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