research-article

Multitask fuzzy Bregman co-clustering approach for clustering data with multisource features

Authors:

Alireza Sokhandan,

Mohammadreza SajadiAuthors Info & Claims

Neurocomputing, Volume 247, Issue C

Pages 102 - 114

https://doi.org/10.1016/j.neucom.2017.03.062

Published: 19 July 2017 Publication History

Abstract

In usual real-world clustering problems, the set of features extracted from the data has two problems which prevent the methods from accurate clustering. First, the features extracted from the samples provide poor information for clustering purpose. Second, the feature vector usually has a high-dimensional multi-source nature, which results in a complex cluster structure in the feature space. In this paper, we propose to use a combination of multi-task clustering and fuzzy co-clustering techniques, to overcome these two problems. In addition, the Bregman divergence is used as the concept of dissimilarity in the proposed algorithm, in order to create a general framework which enables us to use any kind of Bregman distance function, which is consistent with the data distribution and the structure of the clusters. The experimental results indicate that the proposed algorithm can overcome the two mentioned problems, and manages the complexity and weakness of the features, which results in appropriate clustering performances.

References

[1]

J. Zhou, D.S. Wishart, An improved method to detect correct protein folds using partial clustering, BMC Bioinf., 14 (January 2013) 1-12.

[2]

W. Li, L. Fu, B. Niu, S. Wu, J. Wooley, Ultrafast clustering algorithms for metagenomic sequence analysis, Briefings Bioinf., 16 (May 2012) 1-14.

[3]

W.-W. Fan, B. Chen, G. Selvaraj, F.-X. Wu, Discovering biological patterns from short time-series gene expression profiles with integrating PPI data, Neurocomputing, 135 (2014) 3-13.

Digital Library

[4]

G. Ho, W. Ip, C. Lee, W. Mou, Customer grouping for better resources allocation using GA based clustering technique, Expert Syst. Appl., 39 (August 2011) 1979-1987.

Digital Library

[5]

M. Kargari, M.M. Sepehri, Stores clustering using a data mining approach for distributing automotive spare-parts to reduce transportation costs, Expert Syst. Appl., 39 (April 2012) 4740-4748.

Digital Library

[6]

A. Tagarelli, G. Karypis, A segment-based approach to clustering multi-topic documents, Knowl. Inf. Syst., 34 (March 2013) 563-595.

Digital Library

[7]

M. Gong, Y. Liang, J. Shi, W. Ma, Fuzzy C-means clustering with local information and kernel metric for image segmentation, IEEE Trans. Image Proces., 22 (Febuary 2013) 573-584.

Digital Library

[8]

M.I. Lopez, J.M. Luna, C. Romero, S. Ventura, Classification via clustering for predicting final marks based on student participation in forums, in: International Conference on Educational Data Mining Society, 2012.

[9]

D. Pohl, A. Bouchachia, H. Hellwagner, Online indexing and clustering of social media data for emergency management, Neurocomputing, 172 (2016) 168-179.

Digital Library

[10]

A. Liu, Y. Lu, W. Nie, Y. Su, Z. Yang, HEp-2 cells Classification via clustered multi-task learning, Neurocomputing, 195 (2016) 195-201.

Digital Library

[11]

J. Han, X. Ji, X. Hu, J. Han, T. Liu, Clustering and retrieval of video shots based on natural stimulus fMRI, Neurocomputing, 144 (2014) 128-137.

Digital Library

[12]

W. Pedrycz, Collaborative fuzzy clustering, Pattern Recognit. Lett., 23 (December 2002) 1675-1686.

Digital Library

[13]

V. Loia, W. Pedrycz, S. Senatore, Semantic web content analysis: a study in proximity-based collaborative clustering, IEEE Trans. Fuzzy Syst., 15 (December 2007) 1294-1312.

Digital Library

[14]

L. Coletta, E. Hruschka, R. Campello, Collaborative fuzzy clustering algorithms: some refinements and design guidelines, IEEE Trans. Fuzzy Syst., 20 (June 2012) 444-462.

Digital Library

[15]

B. Mandhani, S. Joshi, K. Kummamuru, A matrix density based algorithm to hierarchically co-cluster documents and words, in: International Conference on World Wide Web, 2003.

Digital Library

[16]

W.-C. Tjhi, L. Chen, Dual fuzzy-possibilistic coclustering for categorization of documents, IEEE Trans. Fuzzy Syst., 17 (April 2008) 532-543.

Digital Library

[17]

Y. Yan, L. Chen, W.C. Tjhi, Fuzzy semi-supervised co-clustering for text documents, Fuzzy Sets Syst., 215 (March 2013) 74-89.

Digital Library

[18]

C. Laclau, M. Nadif, Hard and fuzzy diagonal co-clustering for document-term partitioning, Neurocomputing, 193 (2016) 133-147.

Digital Library

[19]

W. Dai, Q. Yang, G.-R. Xue, Y. Yu, Self-taught clustering, in: International Conference on Machine Learning, 2008.

Digital Library

[20]

Q. Gu, J. Zhou, Learning the shared subspace for multi-task clustering and transductive transfer classification, in: IEEE International Conference on Data Mining, 2009.

Digital Library

[21]

Z. Zhang, J. Zhou, Multi-task clustering via domain adaptation, Pattern Recognit., 45 (January 2012) 465-473.

Digital Library

[22]

J. Zhang, C. Zhang, Multitask Bregman clustering, Neurocomputing, 70 (May 2011) 1720-1734.

Digital Library

[23]

X. Zhang, X. Zhang, Smart multi-task Bregman clustering and multi-task kernel clustering, in: Proceedings of the Twenty-Seventh AAAI Conference on Artificial Intelligence, 2013.

Digital Library

[24]

T.N. Huy, H. Shao, B. Tong, E. Suzuki, A feature-free and parameter-light multi-task clustering framework, Knowl. Inf. Syst., 36 (July 2013) 251-276.

Digital Library

[25]

M. Hanmandlu, O.P. Verma, S. Susan, V. Madasu, Color segmentation by fuzzy co-clustering of chrominance color features, Neurocomputing, 120 (November 2013) 235-249.

[26]

H. Izakian, W. Pedrycz, Agreement-based fuzzy C-means for clustering data with blocks of features, Neurocomputing, 120 (March 2014) 266-280.

Digital Library

[27]

A. Banerjee, S. Merugu, I.S. Dhillon, J. Ghosh, Clustering with Bregman divergences, J. Mach. Learn. Res., 6 (December 2005) 1705-1749.

Digital Library

[28]

L. Bregman, The relaxation method of finding the common point of convex sets and its application to the solution of problems in convex programming, USSR Comput. Math. Math. Phys., 7 (1967) 200-217.

[29]

L. Cayton, Fast Nearest Neighbor Retrieval for Bregman Divergences, in: International Conference on Machine Learning, 2008.

Digital Library

[30]

J.C. Bezdek, Pattern Recognition with Fuzzy Objective Function Algorithms, Kluwer Academic Publishers, Norwell, 1981.

Digital Library

[31]

K. Zou, Z. Wang, M. Hu, An new initialization method for fuzzy c-means algorithm, in: Fuzzy Optimization and Decision Making, 7, 2008, pp. 409-416.

Digital Library

[32]

R. Babuska, Fuzzy Clustering, Delft University of Technology, Delft, the Netherlands, 2001.

[33]

F. Nielsen, R. Nock, Sided and symmetrized Bregman centroids, IEEE Trans. Inf. Theory, 55 (June 2009) 2882-2904.

Digital Library

[34]

A. Strehl, J. Ghosh, Cluster ensembles - a knowledge reuse framework for combining multiple partitions, J. Mach. Learn. Res., 3 (March 2003) 583-617.

Digital Library

[35]

O.T. Yldz, . Aslan, E. Alpaydn, Multivariate statistical tests for comparing classification algorithms, Learn. Intell. Optim., 6683 (2011) 1-15.

Digital Library

[36]

D. Arthur, S. Vassilvitskii, k-means++: the advantages of careful seeding, New Orleans, Louisiana, 2007.

[37]

M. Lichman, UCI Machine Learning Repository {http://Archive.Ics.Uci.Edu/Ml}, University of CaliforniaSchool of Information and Computer Science, Irvine, CA, 2013.

[38]

B. Long, Z. Zhang, P.S. Yu, Co-clustering by block value decomposition, in: The Eleventh ACM SIGKDD International Conference on Knowledge Discovery in Data Mining, 2005.

Digital Library

[39]

I.S. Dhillon, S. Mallela, D.S. Modha, Information-theoretic co-clustering, in: International Conference on Knowledge Discovery and Data Mining, 2003.

Digital Library

[40]

J. Li, T. Li, HCC: a hierarchical co-clustering algorithm, in: International ACM SIGIR Conference on Research and Development in Information Retrieval, 2010.

Digital Library

[41]

W. Cheng, X. Zhang, F. Pan, W. Wang, HICC: an entropy splitting-based framework for hierarchical co-clustering, Knowl. Inf. Syst., 46 (2016) 343-367.

Digital Library

[42]

N. Zheng-Yu, J. Dong-Hong, T. Chew-Lim, Document clustering based on cluster validation, in: ACM International Conference on Information and Knowledge Management, 2004.

Digital Library

Cited By

Sugahara KOkamoto K(2023)Hierarchical co-clustering with augmented matrices from external domainsPattern Recognition10.1016/j.patcog.2023.109657142:COnline publication date: 1-Oct-2023
https://dl.acm.org/doi/10.1016/j.patcog.2023.109657
Wu CZhang X(2021)A novel kernelized total Bregman divergence-based fuzzy clustering with local information for image segmentationInternational Journal of Approximate Reasoning10.1016/j.ijar.2021.06.004136:C(281-305)Online publication date: 1-Sep-2021
https://dl.acm.org/doi/10.1016/j.ijar.2021.06.004
Ren JYang Y(2020)Multitask possibilistic and fuzzy co-clustering algorithm for clustering data with multisource featuresNeural Computing and Applications10.1007/s00521-018-3851-032:9(4785-4804)Online publication date: 1-May-2020
https://dl.acm.org/doi/10.1007/s00521-018-3851-0
Show More Cited By

Multitask fuzzy Bregman co-clustering approach for clustering data with multisource features
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Unsupervised learning

Recommendations

Smart Multitask Bregman Clustering and Multitask Kernel Clustering

Traditional clustering algorithms deal with a single clustering task on a single dataset. However, there are many related tasks in the real world, which motivates multitask clustering. Recently some multitask clustering algorithms have been proposed, ...
Multitask possibilistic and fuzzy co-clustering algorithm for clustering data with multisource features
Abstract
People often encounter two major problems for the practical clustering problems. One is the problem arising from improper extraction of feature sets, such as the weakness of the features and the feature vector usually has the property of high-...
Probabilistic consensus clustering using evidence accumulation

Clustering ensemble methods produce a consensus partition of a set of data points by combining the results of a collection of base clustering algorithms. In the evidence accumulation clustering (EAC) paradigm, the clustering ensemble is transformed into ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image Neurocomputing

Neurocomputing Volume 247, Issue C

July 2017

224 pages

ISSN:0925-2312

Issue’s Table of Contents

Copyright © Elsevier B.V.

Publisher

Elsevier Science Publishers B. V.

Netherlands

Publication History

Published: 19 July 2017

Author Tags

Qualifiers

Research-article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

4
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 19 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

Sugahara KOkamoto K(2023)Hierarchical co-clustering with augmented matrices from external domainsPattern Recognition10.1016/j.patcog.2023.109657142:COnline publication date: 1-Oct-2023
https://dl.acm.org/doi/10.1016/j.patcog.2023.109657
Wu CZhang X(2021)A novel kernelized total Bregman divergence-based fuzzy clustering with local information for image segmentationInternational Journal of Approximate Reasoning10.1016/j.ijar.2021.06.004136:C(281-305)Online publication date: 1-Sep-2021
https://dl.acm.org/doi/10.1016/j.ijar.2021.06.004
Ren JYang Y(2020)Multitask possibilistic and fuzzy co-clustering algorithm for clustering data with multisource featuresNeural Computing and Applications10.1007/s00521-018-3851-032:9(4785-4804)Online publication date: 1-May-2020
https://dl.acm.org/doi/10.1007/s00521-018-3851-0
Bhuvaneswari GManikandan G(2018)A novel machine learning framework for diagnosing the type 2 diabetics using temporal fuzzy ant miner decision tree classifier with temporal weighted genetic algorithmComputing10.1007/s00607-018-0599-4100:8(759-772)Online publication date: 1-Aug-2018
https://dl.acm.org/doi/10.1007/s00607-018-0599-4

View Options

View options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

Media

Figures

Other

Tables

View Issue’s Table of Contents