[go: up one dir, main page]

Skip to main content

Cycle Based Clustering Using Reversible Cellular Automata

  • Conference paper
  • First Online:
Cellular Automata and Discrete Complex Systems (AUTOMATA 2020)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12286))

  • 305 Accesses

Abstract

This work proposes cycle based clustering technique using reversible cellular automata (CAs) where ‘closeness’ among objects is represented as objects belonging to the same cycle, that is reachable from each other. The properties of such CAs are exploited for grouping the objects with minimum intra-cluster distance while ensuring that limited number of cycles exist in the configuration-space. The proposed algorithm follows an iterative strategy where the clusters with closely reachable objects of previous level are merged in the present level using an unique auxiliary CA. Finally, it is observed that, our algorithm is at least at par with the best algorithm existing today.

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

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Adak, S., Mukherjee, S., Das, S.: Do there exist non-linear maximal length cellular automata? A study. In: Mauri, G., El Yacoubi, S., Dennunzio, A., Nishinari, K., Manzoni, L. (eds.) ACRI 2018. LNCS, vol. 11115, pp. 289–297. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-99813-8_26

    Chapter  Google Scholar 

  2. Brock, G., Pihur, V., Datta, S., Datta, S.: clValid: an R package for cluster validation. J. Stat. Softw. 25(4), 1–22 (2008)

    Article  Google Scholar 

  3. Carvalho, F., Lechevallier, Y., Melo, F.: Partitioning hard clustering algorithms based on multiple dissimilarity matrices. Pattern Recogn. 45(1), 447–464 (2012)

    Article  Google Scholar 

  4. Das, S.: Theory and applications of nonlinear cellular automata in VLSI design. PhD thesis, Bengal Engineering and Science University, Shibpur, India (2006)

    Google Scholar 

  5. Dougherty, J., Kohavi, R., Sahami, M.: Supervised and unsupervised discretization of continuous features. In: Machine Learning Proceedings 1995, pp. 194–202. Elsevier (1995)

    Google Scholar 

  6. Dunn, J.C.: Well separated clusters and fuzzy partitions. J. Cybern. 4, 95–104 (1974)

    Article  MathSciNet  Google Scholar 

  7. Dunn, J.C.: Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J. Comput. Appl. Math. 20, 53–65 (1987)

    Article  Google Scholar 

  8. Estivill-Castro, V.: Why so many clustering algorithms - a position paper. ACM SIGKDD Explor. Newslett. 4, 65–75 (2002)

    Article  Google Scholar 

  9. Handl, J., Knowles, J., Kell, D.B.: Computational cluster validation in post-genomic data analysis. Bioinformatics 21(15), 3201–3212 (2005)

    Article  Google Scholar 

  10. Jain, A.K., Murthy, M.N., Flynn, P.J.: Data clustering: a review. ACM Comput. Surv. 31(3), 165–193 (1999)

    Article  Google Scholar 

  11. Wolfram, S.: Theory and applications of cellular automata. World Scientific, Singapore (1986). ISBN 9971-50-124-4 pbk

    Google Scholar 

  12. Xu, D., Tian, Y.: A comprehensive survey of clustering algorithms. Ann. Data Sci. 2(2), 165–193 (2015). https://doi.org/10.1007/s40745-015-0040-1

    Article  MathSciNet  Google Scholar 

  13. Xu, R., Wunsch, D.: Survey of clustering algorithms. IEEE Trans. Neural Net. 16(3), 645–678 (2005)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Sukanya Mukherjee .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2020 IFIP International Federation for Information Processing

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Mukherjee, S., Bhattacharjee, K., Das, S. (2020). Cycle Based Clustering Using Reversible Cellular Automata. In: Zenil, H. (eds) Cellular Automata and Discrete Complex Systems. AUTOMATA 2020. Lecture Notes in Computer Science(), vol 12286. Springer, Cham. https://doi.org/10.1007/978-3-030-61588-8_3

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-61588-8_3

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-61587-1

  • Online ISBN: 978-3-030-61588-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics