Skip to main content
SpringerLink
Log in

Multiple clustering and selecting algorithms with combining strategy for selective clustering ensemble

  • Foundations
  • Published:
Soft Computing Aims and scope Submit manuscript

Abstract

Clustering ensemble can overcome the instability of clustering and improve clustering performance. With the rapid development of clustering ensemble, we find that not all clustering solutions are effective in their final result. In this paper, we focus on selection strategy in selective clustering ensemble. We propose a multiple clustering and selecting approach (MCAS), which is based on different original clustering solutions. Furthermore, we present two combining strategies, direct combining and clustering combining, to combine the solutions selected by MCAS. These combining strategies combine results of MCAS and get a more refined subset of solutions, compared with traditional selective clustering ensemble algorithms and single clustering and selecting algorithms. Experimental results on UCI machine learning datasets show that the algorithm that uses multiple clustering and selecting algorithms with combining strategy performs well on most datasets and outperforms most selective clustering ensemble algorithms.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  • Akbari E, Dahlan HM, Ibrahim R, Alizadeh H (2015) Hierarchical cluster ensemble selection. Eng Appl Artif Intell 39(39):146–156

    Article  Google Scholar 

  • Ali B, Behrooz M-B, Mehdi H, Hamid P (2019) Elite fuzzy clustering ensemble based on clustering diversity and quality measures. Appl Intell 49:1724–1747

    Article  Google Scholar 

  • Alizadeh H, Minaei-Bidgoli B, Parvin H (2013) Optimizing fuzzy cluster ensemble in string representation. Int J Pattern Recogn Artif Intell 27(02):151–156

    Article  MathSciNet  Google Scholar 

  • Alizadeh H, Minaeibidgoli B, Parvin H (2014) To improve the quality of cluster ensembles by selecting a subset of base clusters. J Exp Theor Artif Intell 26(1):127–150

    Article  Google Scholar 

  • Alizadeh H, Minaei-Bidgoli B, Parvin H (2014) Cluster ensemble selection based on a new cluster stability measure. Intell Data Anal 18(3):309–408

    Article  Google Scholar 

  • Azimi J, Fern X (2009) Adaptive cluster ensemble selection. In: International joint conference on artifical intelligence, pp 992–997

  • Bagherinia A, Minaei-Bidgoli B, Hosseinzadeh M, Parvin H (2020) Reliability-based fuzzy clustering ensemble. Fuzzy Sets Syst. https://doi.org/10.1016/j.fss.2020.03.008

    Article  Google Scholar 

  • Bertoni A, Valentini G (2006) Ensembles based on random projections to improve the accuracy of clustering algorithms. Lect Notes Comput Sci 3931:31–37

    Article  Google Scholar 

  • Dai Q, Zhang T, Liu N (2015) A new reverse reduce-error ensemble pruning algorithm. Appl Soft Comput 28:237–249

    Article  Google Scholar 

  • Devi RDH, Deepika P (2016) Performance comparison of various clustering techniques for diagnosis of breast cancer. In: IEEE international conference on computational intelligence and computing research, pp 1–5

  • Faceli K, Sakata TC, Souto MCPD (2010) Partitions selection strategy for set of clustering solutions. Neurocomputing 73(16):2809–2819

    Article  Google Scholar 

  • Fern XZ, Lin W (2008) Cluster ensemble selection, statistical analysis & data mining the Asa. Data Sci J 1(3):128–141

    Google Scholar 

  • Fred ALN, Jain AK (2002) Data clustering using evidence accumulation. In: 16th International conference on pattern recognition, pp 40276

  • Fred ALN, Jain AK (2005) Combining multiple clusterings using evidence accumulation. IEEE Trans Pattern Anal Mach Intell 27(6):835

    Article  Google Scholar 

  • Hadjitodorov ST, Kuncheva LI, Todorova LP (2006) Moderate diversity for better cluster ensembles. Inf Fus 7(3):264–275

    Article  Google Scholar 

  • He L, Zhang H (2016) Iterative ensemble normalized cuts. Pattern Recogn 52:274–286

    Article  MATH  Google Scholar 

  • Hong Y, Kwonga S (2008) To combine steady-state genetic algorithm and ensemble learning for data clustering. Pattern Recogn Lett 29(9):1416–1423

    Article  Google Scholar 

  • Hong Y, Kwong S, Wang H, Ren Q (2009) Resampling-based selective clustering ensembles. Pattern Recogn Lett 30(3):298–305

    Article  Google Scholar 

  • Hu J, Li T, Wang H, Fujita H (2016) Hierarchical cluster ensemble model based on knowledge granulation. Knowl-Based Syst 91:179–188

    Article  Google Scholar 

  • Huang S, Wang H, Li D, Yang Y, Li T (2015) Spectral co-clustering ensemble. Knowl-Based Syst 84:46–55

    Article  Google Scholar 

  • Huang D, Wang C-D, Wu J, Lai J-H, Kwoh CK (2019) Ultra-scalable spectral clustering and ensemble clustering. IEEE Transactions on Knowledge & Data Engineering 32(6):1212–1226

    Article  Google Scholar 

  • Hung C (2015) A constrained growing grid neural clustering model. Appl Intell 43(1):15–31

    Article  Google Scholar 

  • Jia J, Xiao X, Liu B, Jiao L (2011) Bagging-based spectral clustering ensemble selection. Pattern Recogn Lett 32(10):1456–1467

    Article  Google Scholar 

  • Kuncheva LI, Hadjitodorov ST (2004) Using diversity in cluster ensembles. In: IEEE international conference on systems, man and cybernetics vol 2, pp 1214–1219

  • Liu H, Wu J, Liu T, Tao D, Fu Y (2017) Spectral ensemble clustering via weighted k-means: theoretical and practical evidence. IEEE Trans Knowl Data Eng 29(5):1129–1143

    Article  Google Scholar 

  • Lv Y, Ma T, Tang M, Cao J, Tian Y, Al-Dhelaan A, Al-Rodhaan M (2016) An efficient and scalable density-based clustering algorithm for datasets with complex structures. Neurocomputing 171:9–22

    Article  Google Scholar 

  • Ma T, Zhang Y, Cao J, Shen J, Tang M, Tian Y, Al-Dhelaan A, Al-Rodhaan M (2015) KDVEM : a k-degree anonymity with vertex and edge modification algorithm. Computing 97(12):1165–1184

    Article  MathSciNet  MATH  Google Scholar 

  • Ma T, Jia J, Xue Y, Tian Y, Al-Dhelaan A, Al-Rodhaan M (2018) Protection of location privacy for moving knn queries in social networks. Appl Soft Comput 66:525–532

    Article  Google Scholar 

  • Ma T, Shao W, Hao Y, Cao J (2018) Graph classification based on graph set reconstruction and graph kernel feature reduction. Neurocomputing 296:33–45

    Article  Google Scholar 

  • Ma T, Zhao Y, Zhou H, Tian Y, Al-Dhelaan A, Al-Rodhaan M (2019) Natural disaster topic extraction in sina microblogging based on graph analysis. Expert Syst Appl 115:346–355

    Article  Google Scholar 

  • Ma T, Liu Q, Cao J, Tian Y, Al-Dhelaan A (2020) MznahAl-Rodhaan, Lgiem: global and local node influence based community detection. Fut Gener Comput Syst 105:533–546

    Article  Google Scholar 

  • Meng J, Hao H, Luan Y (2016) Classifier ensemble selection based on affinity propagation clustering. J Biomed Inform 60:234–242

    Article  Google Scholar 

  • Minaei-Bidgoli B (2016) A new selection strategy for selective cluster ensemble based on diversity and independency. Eng Appl Artif Intell 56:260–272

    Article  Google Scholar 

  • Muhammad Y, Ali R, Daoqiang Z, Minaei-Bidgoli B (2016) A new selection strategy for selective cluster ensemble based on diversity and independency. Eng Appl Artif Intell 56:260–272

    Article  Google Scholar 

  • Naldi AC, Carvalho RJ (2013) Campello, Cluster ensemble selection based on relative validity indexes. Data Min Knowl Disc 27(2):259–289

    Article  MATH  Google Scholar 

  • Nazari A, Dehghan A, Nejatian S (2019) A comprehensive study of clustering ensemble weighting based on cluster quality and diversity. Pattern Anal Applic 22:133–145

    Article  MathSciNet  Google Scholar 

  • Rong H, Ma T, Cao J, Tian Y, Al-Dhelaan A, Al-Rodhaan M (2019) Deep rolling: a novel emotion prediction model for a multi-participant communication context. Inf Sci 488:158–180

    Article  Google Scholar 

  • Rong H, Hao Y, Cao J, Tia Y, Al-Rodhaan M (2019) A novel sentiment polarity detection framework for chinese. IEEE Trans Affect Comput. https://doi.org/10.1109/TAFFC.2019.2932061

    Article  Google Scholar 

  • Soltanmohammadi E, Naraghi-Pour M, Schaar MVD (2016) Context-based unsupervised ensemble learning and feature ranking. Mach Learn 105(3):1–27

    Article  MathSciNet  MATH  Google Scholar 

  • Strehl A, Ghosh J (2003) Cluster ensembles—a knowledge reuse framework for combining multiple partitions. JMLR 3:583–617

    MathSciNet  MATH  Google Scholar 

  • Topchy A, Jain AK, Punch W (2003) Combining multiple weak clusterings. In: IEEE international conference on data mining, pp 331–338

  • Wang LJ, Hao ZF, Cai RC, Wen W (2014) An improved local adaptive clustering ensemble based on link analysis. In: International conference on machine learning and cybernetics, pp 10–15

  • Wang H, Qi J, Zheng W, Wang M (2010) Semi-supervised cluster ensemble based on binary similarity matrix. In: The IEEE international conference on information management and engineering, pp 251–254

  • Wei T (2005) Bagging-based selective clusterer ensemble. J Softw 16(4):496–502

    Article  Google Scholar 

  • Wu XX, Ni ZW, Ni LP, Zhang C (2014) Research on selective clustering ensemble algorithm based on normalized mutual information and fractal dimension. Pattern Recog Artif Intell 27(9):847–855

    Google Scholar 

  • Xu S, Chan KS, Gao J, Xu X, Li X, Hua X, An J (2016) An integrated k-means-laplacian cluster ensemble approach for document datasets. Neurocomputing 214:495–507

    Article  Google Scholar 

  • Yang F, Li T, Zhou Q, Xiao H (2017) Cluster ensemble selection with constraints. Neurocomputing 235:59–70

    Article  Google Scholar 

  • Yousefnezhad M, Huang S-J, Zhang D (2017) A framework for clustering ensemble by exploiting the wisdom of crowds theory. IEEE Trans Cybern 48(2):133–145

    Google Scholar 

  • Yu Z, Chen H, You J, Wong HS (2014) Double selection based semi-supervised clustering ensemble for tumor clustering from gene expression profiles. IEEE/ACM Trans Comput Biol Bioinf 11(4):727–740

    Article  Google Scholar 

  • Yu Z, Li L, Gao Y, You J, Liu J, Wong HS, Han G (2014) Hybrid clustering solution selection strategy. Pattern Recogn 47(10):3362–3375

    Article  Google Scholar 

  • Yu Z, Zhu X, Wong HS, You J, Zhang J, Han G (2016) Distribution-based cluster structure selection. IEEE Trans Cybern 47(11):3554–3567

    Article  Google Scholar 

  • Yu Z, Luo P, You J, Wong HS, Leung H, Wu S, Zhang J, Han G (2016) Incremental semi-supervised clustering ensemble for high dimensional data clustering. IEEE Trans Knowl Data Eng 28(3):701–714

    Article  Google Scholar 

  • Zhang H, Cao L (2014) A spectral clustering based ensemble pruning approach. Neurocomputing 139:289–297

    Article  Google Scholar 

  • Zhang S, Yang L, Xie D (2015) Unsupervised evaluation of cluster ensemble solutions. In: Seventh international conference on advanced computational intelligence, 2015, pp 101–106

  • Zhou ZH, Tang W (2006) Clusterer ensemble. Knowl-Based Syst 19(1):77–83

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported in part by National Science Foundation of China (No. U1736105) and also supported by the National Social Science Foundation of China (No. 16ZDA054). The authors extend their appreciation to the Deanship of Scientific Research at King Saud University for funding this work through Research Group No. RGP-264.

Author information

Authors and Affiliations

  1. School of Computer, Nanjing University of information science and Technology, Jiangsu, 210-044, Nanjing, China

    Tinghuai Ma, Te Yu & Xiuge Wu

  2. School of Economics and Management, Nanjing University of Information Science and Technology, Nanjing, 210044, China

    Jie Cao

  3. Computer Science Department, College of Computer and Information Science, King Saud University, Riyadh, 11362, Saudi Arabia

    Alia Al-Abdulkarim, Abdullah Al-Dhelaan & Mohammed Al-Dhelaan

Authors
  1. Tinghuai Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Te Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xiuge Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jie Cao
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Alia Al-Abdulkarim
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Abdullah Al-Dhelaan
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Mohammed Al-Dhelaan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Tinghuai Ma.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Communicated by A. Di Nola.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ma, T., Yu, T., Wu, X. et al. Multiple clustering and selecting algorithms with combining strategy for selective clustering ensemble. Soft Comput 24, 15129–15141 (2020). https://doi.org/10.1007/s00500-020-05264-1

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-020-05264-1

Keywords

Search

Navigation