Skip to main content
SpringerLink
Log in

Detection of pre-cluster nano-tendency through multi-viewpoints cosine-based similarity approach

  • Original Paper
  • Published:
Nanotechnology for Environmental Engineering Aims and scope Submit manuscript

Abstract

Pre-clusters assessment is a significant problem in data clustering. It found that visual cluster tendency assessment (VAT) is majorly focused on addressing the problem of pre-clusters assessment. This visual technique initially derives the similarity features of data objects using either cosine or Euclidean distance metrics. Cosine is considering both magnitudes and direction of the vectors; thus, it greatly succeeded in data clustering applications. Only a single viewpoint (i.e., origin) is used in the cosine metric. Finding the similarity features using multiple viewpoints is more accurate than a single viewpoint cosine metric. This paper presents the multi-viewpoints cosine-based similarity VAT (MVS-VAT) which considers the multi-viewpoints for an effective assessment of nano-pre-clusters (or nano-cluster tendency). Clustering accuracy (CA) and normalized mutual information (NMI) are taken for measuring the performance of the existing and proposed methods. It is proved that the efficiency of the proposed MVS-VAT is improved from 20 to 40% compared to VAT and cVAT concerning the parameters of CA and NMI. Therefore, the quality of data clusters is obtained through the proposed technique MVS-VAT. Experimental is conducted on several benchmarked datasets for illustration of an empirical study of the existing and proposed techniques.

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
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Wu X, Kumar V, Quinlan JR et al (2008) Top 10 algorithms in data mining, knowledge information system, vol 14. Springer, Heidelberg, pp 1–37

    Google Scholar 

  2. Rui X, Wunsch D (2005) Survey of clustering algorithms. IEEE Trans Neural Netw 16(3):645–678

    Article  Google Scholar 

  3. Bhatnagar V, Majhi R, Jena PR (2018) Comparative performance evaluation of clustering algorithms for grouping manufacturing frms. Arab J Sci Eng 43:4071–4083

    Article  Google Scholar 

  4. Rajendra Prasad K, Suleman Basha M (2016) Improving the performance of speech clustering method. In: IEEE-10th international conference on intelligent systems and control (ISCO)

  5. Bezdek J, Pattern recognition with objective function algorithms. Plenum, New York

  6. Kumar D, Palaniswami M, Rajasegarar S, Leckie C, Bezdek JC, Havens TC (2013) clusiVAT: a mixed visual/numerical clustering algorithm for big data. In: 2013 IEEE international conference on big data, Silicon Valley, pp 112–117

  7. Rathore P, Bezdek JC, Palaniswami M (2021) Fast cluster tendency assessment for big, high-dimensional data. In: Lesot MJ, Marsala C (eds) Fuzzy approaches for soft computing and approximate reasoning: theories and applications. Studies in fuzziness and soft computing, vol 394. Springer, Cham. https://doi.org/10.1007/978-3-030-54341-9_12

    Chapter  Google Scholar 

  8. Rathore P, Kumar D, Bezdek JC, Rajasegarar S, Palaniswami M (2019) A Rapid hybrid clustering algorithm for large volumes of high dimensional data. IEEE Trans Knowl Data Eng 31(4):641–654

    Article  Google Scholar 

  9. Bezdek JC, Hathaway RJ (2002) VAT: a tool for visual assessment of (cluster) tendency. In: Proceedings of the 2002 international joint conference on neural networks, Honolulu, pp 2225–2230

  10. Suleman Basha M, Mouleeswaran SK, Rajendra Prasad K (2019) Cluster tendency methods for visualizing the data partitions. Int J Innov Technol Explor Eng 8(11):2978–2982

    Article  Google Scholar 

  11. Suleman Basha M, Rajendra Prasad K (2018) Efficient cluster tendency methods for discovering the number of clusters. ARPN J Eng Appl Sci 13(4):1327–1334

    Google Scholar 

  12. Havens TC, Bezdek JC (2012) An efficient formulation of the improved visual assessment of cluster tendency (iVAT) algorithm. IEEE Trans Knowl Data Eng 24(5):813–822

    Article  Google Scholar 

  13. Kumar D, Bezdek JC, Palaniswami M, Rajasegarar S, Leckie C, Havens TC (2016) A hybrid approach to clustering in big data. IEEE Trans Cybern 46(10):2372–2385

    Article  Google Scholar 

  14. Rahamat Basha S et al (2020) A comparative approach of text mining: classification, clustering and extraction techniques. J Mech Continua Math Sci. https://doi.org/10.26782/jmcms.spl.5/2020.01.00010

    Article  Google Scholar 

  15. Narasimhulu K et al (2021) An enhanced cosine-based visual technique for the robust tweets data clustering. Int J Intell Comput Cybern 14(2):170–184

    Article  Google Scholar 

  16. Rajendra Prasad K, Mohammed M, Noorullah RM (2019) Visual topic models for healthcare data clustering. Evolut Intell. https://doi.org/10.1007/s12065-019-00300-y

    Article  Google Scholar 

  17. Suleman Basha M, Mouleeswaran SK, Rajendra Prasad K (2021) Sampling-based visual assessment computing techniques for an efficient social data clustering. J Supercomput 77:8013–8037

    Article  Google Scholar 

  18. Prasad K, Mohammed M, Prasad L, Anguraj DK (2021) An efficient sampling-based visualization technique for big data clustering with crisp partitions. Distrib Parallel Databases. https://doi.org/10.1007/s10619-021-07324-3

    Article  Google Scholar 

  19. https://www.webmd.com/

  20. Eswara Reddy B, Rajendra Prasad K (2016) Improving the performance of visualized clustering method. Int J Syst Assur Eng Manag 7(1):102–111

    Article  Google Scholar 

  21. Asuncion A, Newman D (2007) UCI machine learning repository

  22. Pattanodom et al (2016) Clustering data with the presence of missing values by ensemble approach. In: 2016 Second Asian conference on defence technology

  23. Amelio A, Pizzuti C (2015) Is normalized mutual information a fair measure for comparing community detection methods? In: IEEE/ACM international conference on advances in social networks analysis and mining

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Dayananda Sagar University, Bangalore, India

    M. Suleman Basha & S. K. Mouleeswaran

  2. Department of Computer Science and Engineering, Rajeev Gandhi Memorial College of Engineering and Technology, Nandyal, India

    K. Rajendra Prasad

Authors
  1. M. Suleman Basha
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. S. K. Mouleeswaran
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. K. Rajendra Prasad
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to K. Rajendra Prasad.

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

Basha, M.S., Mouleeswaran, S.K. & Prasad, K.R. Detection of pre-cluster nano-tendency through multi-viewpoints cosine-based similarity approach. Nanotechnol. Environ. Eng. 7, 259–268 (2022). https://doi.org/10.1007/s41204-022-00222-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s41204-022-00222-8

Keywords

Search

Navigation