Skip to main content
SpringerLink
Log in

An Improved Mean Imputation Clustering Algorithm for Incomplete Data

  • Published:
Neural Processing Letters Aims and scope Submit manuscript

Abstract

There are many incomplete data sets in all fields of scientific studies due to random noise, data lost, limitations of data acquisition, data misunderstanding etc. Most of the clustering algorithms can not be used for incomplete data sets directly because objects with missing values need to be preprocessed. For this reason, this paper presents an improved mean imputation clustering algorithm for incomplete data based on partition clustering algorithm. In the proposed method, we divide the universe into two sets: the set of objects with non-missing values and the set of objects with missing values. Firstly, the objects with non-missing values are clustered by traditional clustering algorithm. For each object with missing values, we use the mean attribute’s value of each cluster to fill the missing attribute’s value based on the cluster results of the objects with non-missing values, respectively. Perturbation analysis of cluster centroid is applied to search the optimal imputation. The experimental clustering results on some UCI data sets are evaluated by several validity indexes, which proves the effectiveness of the proposed algorithm.

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

Similar content being viewed by others

References

  1. Xu R, Wunsch D (2005) Survey of clustering algorithms. IEEE Trans Neural Netw 16:645–678

    Article  Google Scholar 

  2. Aggarwal CC, Reddy CK (2013) Data clustering: algorithms and applications. Chapman & Hall/CRC Press, Boca raton

    Book  Google Scholar 

  3. Wang PX, Yao YY (2018) CE3: a three-way clustering method based on mathematical morphology. Knowl-Based Syst 155:54–65

    Article  Google Scholar 

  4. Wang PX, Shi H, Yang XB, Mi JS (2019) Three-way k-means: integrating k-means and three-way decision. Int J Mach Learn Cybernet 10:2767–2777

    Article  Google Scholar 

  5. Yang XB, Qi YS, Song XN, Yang JY (2013) Test cost sensitive multigranulation rough set: model and minimal cost selection. Inf Sci 250:184–199

    Article  MathSciNet  Google Scholar 

  6. Qian YH, Cheng HH, Wang JT, Liang JY, Pedrycz W, Dang CY (2017) Grouping granular structures in human granulation intelligence. Inf Sci 382–383:150–169

    Article  Google Scholar 

  7. Yang XB, Yao YY (2018) Ensemble selector for attribute reduction. Appl Soft Comput 70:1–11

    Article  Google Scholar 

  8. Elalami ME (2011) Supporting image retrieval framework with rule base system. Knowl-Based Syst 24:331–340

    Article  Google Scholar 

  9. Sebiskveradze D, Vrabie V, Gobinet C (2011) Automation of an algorithm based on fuzzy clustering for analyzing tumoral heterogeneity in human skin carcinoma tissue sections. Lab Investig 91:799–811

    Article  Google Scholar 

  10. Kalyani S, Swarup KS (2011) Particle swarm optimization based k-means clustering approach for security assessment in power systems. Expert Syst Appl 38:10839–10846

    Article  Google Scholar 

  11. Wu YH (2015) General overview on clustering algorithms. Comput Sci 42:491–499

    Google Scholar 

  12. Jain AK (2008) Data clustering: 50 years beyond k-means. In: 2008 European conference on machine learning and principles and practice of knowledge discovery in databases. Springer, Berlin, pp 3–4

  13. Macqueen J (1967) Some methods for classification and analysis of multi-variate observations. In: 1967 Proceeding of Berkeley symposium on mathematical statistics and probability conference, pp 281–297

  14. Arthur D, Vassilvitskii S (2007) K-Means++: the advantages of careful seeding. In: ACM-SIAM symposium on discrete algorithms (SODA’07), New Orleans, LA, pp 1027–1035

  15. Park HS, Jun CH (2009) A simple and fast algorithm for K-medoids clustering. Expert Syst Appl 36:3336–3341

    Article  Google Scholar 

  16. Yu SS, Chu SW, Wang CM, Chan YK, Chang TC (2018) Two improved k-means algorithms. Appl Soft Comput 68:747–755

    Article  Google Scholar 

  17. Franti P, Sieranoja S (2019) How much can k-means be improved by using better initialization and repeats? Pattern Recogn 93:95–112

    Article  Google Scholar 

  18. Chao GQ (2019) Discriminative k-means laplacian clustering. Neural Process Lett 49:393–405

    Article  Google Scholar 

  19. Honarkhah M, Caers J (2010) Stochastic simulation of patterns using distance-based pattern modeling. Math Geosci 42:487–517

    Article  Google Scholar 

  20. Rubin DB (1976) Inference and missing data. Biometrika 63:581–592

    Article  MathSciNet  Google Scholar 

  21. Little RJA, Rubin DB (2002) Statistical analysis with missing data. Technometrics 45:364–365

    MATH  Google Scholar 

  22. Pang XS (2012) Comparative study on interpolation processing method of missing data. Stat Decis 24:18–22

    Google Scholar 

  23. Dempster A (1977) Maximum likelihood from incomplete data via the EM algorithm. J R Stat Soc 39:1–38

    MathSciNet  MATH  Google Scholar 

  24. Grzymala-Busse JW, Fu M (2000) A comparison of several approaches to missing attribute values in data mining. In: Proceedings of the 2nd international conference on rough sets and current trends in computing. Springer, Berlin, pp 378–385

  25. Doquire G, Verleysen M (2012) Feature selection with missing data using mutual information estimators. Neurocomputing 90:3–11

    Article  Google Scholar 

  26. Jason VH, Khoshgoftaar TM (2014) Incomplete-case nearest neighbor imputation in software measurement data. Inf Sci 259:586–610

    Google Scholar 

  27. Hathaway RJ, Bezdek JC (2001) Fuzzy c-means clustering of incomplete data. IEEE Trans Syst Man Cybern Part B Cybern 31:735–744

    Article  Google Scholar 

  28. Zhang DQ, Chen SC (2003) Clustering incomplete data using kernel-based fuzzy c-means algorithm. Neural Process Lett 18:155–162

    Article  Google Scholar 

  29. Li D, Gu H, Zhang LY (2010) A fuzzy c-means clustering algorithm based on nearest-neighbor intervals for incomplete data. Expert Syst Appl 37:6942–6947

    Article  Google Scholar 

  30. Li D, Zhang LY, Gu H (2012) An attribute weighted fuzzy c-means algorithm for incomplete data clustering. J Dalian Univ Technol 52:449–453

    MathSciNet  Google Scholar 

  31. Li D, Gu H, Zhang LY (2013) A hybrid genetic algorithm fuzzy c-means approach for incomplete data clustering based on nearest-neighbor intervals. Soft Comput 17:1787–1796

    Article  Google Scholar 

  32. Li JH, Song SJ, Zhang YL, Li K (2017) A robust fuzzy c-means clustering algorithm for incomplete data. In: 2017 International conference on life system modeling and simulation & 2017 international conference on intelligent computing for sustainable energy and environment, vol 762, pp 3–12 (2017)

  33. Su T, Yu H (2016) Three-way decision clustering algorithm for incomplete data based on q-nearest neighbors. J Frontiers Comput Sci Technol 10:875–883

    Google Scholar 

  34. Shi QY, Liang JY, Zhao XW (2016) A clustering ensemble algorithm for incomplete mixed data. J Comput Res Develop 53:1979–1989

    Google Scholar 

  35. Mesquita DPP, Gomes JPP, Rodrigues LR (2019) Artificial neural networks with random weights for incomplete datasets. Neural Process Lett. https://doi.org/10.1007/s11063-019-10012-0

    Article  Google Scholar 

  36. Davies DL, Bouldin DW (1979) A cluster separation measure. IEEE Trans Pattern Anal Mach Intell 1:224–227

    Article  Google Scholar 

  37. Rousseeuw PJ (1987) Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J Comput Appl Math 20:53–65

    Article  Google Scholar 

  38. Bache K, Lichman M (2013) UCI machine learning repository. University of California, School of Information and Computer Science. http://archive.ics.uci.edu/ml

Download references

Acknowledgements

The authors would like to thank the editor and the anonymous reviewers for their constructive and valuable comments. This work was supported in part by National Natural Science Foundation of China (Nos. 61503160 and 61773012), Natural Science Foundation of the Jiangsu Higher Education Institutions of China (No. 15KJB110004), Postgraduate Research & Practice Innovation Program of Jiangsu Province (No. KYCX19_1699).

Author information

Authors and Affiliations

  1. School of Computer Science, Jiangsu University of Science and Technology, Zhenjiang, 212003, People’s Republic of China

    Hong Shi, Xin Yang & Hualong Yu

  2. School of Science, Jiangsu University of Science and Technology, Zhenjiang, Jiangsu, 212003, People’s Republic of China

    Pingxin Wang

  3. College of Mathematics and Information Science, Hebei Normal University, Shijiazhuang, 050024, People’s Republic of China

    Pingxin Wang

Authors
  1. Hong Shi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Pingxin Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xin Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hualong Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Pingxin Wang.

Additional information

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

Shi, H., Wang, P., Yang, X. et al. An Improved Mean Imputation Clustering Algorithm for Incomplete Data. Neural Process Lett 54, 3537–3550 (2022). https://doi.org/10.1007/s11063-020-10298-5

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11063-020-10298-5

Keywords

Search

Navigation