Skip to main content
SpringerLink
Log in
Book cover

Australasian Conference on Artificial Life and Computational Intelligence

ACALCI 2015: Artificial Life and Computational Intelligence pp 373–386Cite as

The MST-kNN with Paracliques

  • Conference paper

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8955))

Abstract

In this work, we incorporate new edges from a paraclique-identification approach to the output of the MST-kNN graph partitioning method. We present a statistical analysis of the results on a dataset originated from a computational linguistic study of 84 Indo-European languages. We also present results from a computational stylistic study of 168 plays of the Shakespearean era. For the latter, results of the Kruskal-Wallis test 1 (observed vs. all permutations) showed a p-value of a 1.62E-11 and a Wilcoxon test a p-value of 8.1E-12. Overall, our results clearly show in both cases that the modified approach provides statistically more significant results than the use of the MST-kNN alone, thus providing a highly-scalable alternative and statistically sound approach for data clustering.

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

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 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

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Anders, K.-H.: A hierarchical graph-clustering approach to find groups of objects. In: Proceedings 5th Workshop on Progress in Automated Map Generalization, pp. 1–8 (2003)

    Google Scholar 

  2. Arefin, A.S., Inostroza-Ponta, M., Mathieson, L., Berretta, R., Moscato, P.: Clustering nodes in large-scale biological networks using external memory algorithms. In: Xiang, Y., Cuzzocrea, A., Hobbs, M., Zhou, W. (eds.) ICA3PP 2011, Part II. LNCS, vol. 7017, pp. 375–386. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  3. Arefin, A.S., Mathieson, L., Johnstone, D., Berretta, R., Moscato, P.: Unveiling clusters of RNA transcript pairs associated with markers of Alzheimers disease progression. PloS One 7(9), e45535 (2012)

    Google Scholar 

  4. Arefin, A.S., Riveros, C., Berretta, R., Moscato, P.: kNN-MST-Agglomerative: A fast and scalable graph-based data clustering approach on GPU. In: 2012 7th International Conference on Computer Science & Education (ICCSE), pp. 585–590. IEEE (2012)

    Google Scholar 

  5. Arefin, A.S., Vimieiro, R., Riveros, C., Craig, H., Moscato, P.: An Information Theoretic clustering approach for unveiling authorship affnities in Shakespearean era plays and poems. PLoS ONE 9(10), e111445 (2014)

    Google Scholar 

  6. Arefin, A.S., Riveros, C., Berretta, R., Moscato, P.: kNN-borůvka-GPU: A fast and scalable MST construction from kNN graphs on GPU. In: Murgante, B., Gervasi, O., Misra, S., Nedjah, N., Rocha, A.M.A.C., Taniar, D., Apduhan, B.O. (eds.) ICCSA 2012, Part I. LNCS, vol. 7333, pp. 71–86. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  7. Berkhin, P.: A survey of clustering data mining techniques. In: Grouping multidimensional data, pp. 25–71. Springer (2006)

    Google Scholar 

  8. Bryant, D., Filimon, F., Gray, R.D.: Untangling our past: languages, trees, splits and networks. In: The Evolution of Cultural Diversity: Pylogenetic Approaches, pp. 67–84 (2005)

    Google Scholar 

  9. Capp, A., Inostroza-Ponta, M., Bill, D., Moscato, P., Lai, C., Christie, D., Lamb, D., Turner, S., Joseph, D., Matthews, J.: Is there more than one proctitis syndrome? a revisitation using data from the TROG 96.01 trial. Radiotherapy and Oncology 90(3), 400–407 (2009)

    Article  Google Scholar 

  10. Chesler, E., Langston, M.: Combinatorial genetic regulatory network analysis tools for high throughput transcriptomic data. In: Eskin, E., Ideker, T., Raphael, B., Workman, C. (eds.) RECOMB 2005. LNCS (LNBI), vol. 4023, pp. 150–165. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  11. Craig, H., Whipp, R.: Old spellings, new methods: automated procedures for indeterminate linguistic data. Literary and Linguistic Computing 25(1), 37–52 (2010)

    Article  Google Scholar 

  12. Csardi, G., Nepusz, T.: The igraph software package for complex network research. Inter Journal, Complex Systems 1695(5) (2006)

    Google Scholar 

  13. Dyen, I., Kruskal, J.B., Black, P.: An Indoeuropean classification: a lexicostatistical experiment. Transactions of the American Philosophical Society, iii–132 (1992)

    Google Scholar 

  14. Feige, U., Goldwasser, S., Lovsz, L., Safra, S., Szegedy, M.: Approximating clique is almost NP-complete. In: 2013 IEEE 54th Annual Symposium on Foundations of Computer Science, pp. 2–12. IEEE Comput. Soc. Press (1991)

    Google Scholar 

  15. Gonzlez-Barrios, J.M., Quiroz, A.J.: A clustering procedure based on the comparison between the k nearest neighbors graph and the minimal spanning tree. Statistics & Probability Letters 62(1), 23–34 (2003)

    Article  MathSciNet  Google Scholar 

  16. Hollander, M., Wolfe, D.A., Chicken, E.: Nonparametric statistical methods, vol. 751. John Wiley & Sons (2013)

    Google Scholar 

  17. Huh, M.H., Jhun, M.: Random permutation testing in multiple linear regression. Communications in Statistics-Theory and Methods 30(10), 2023–2032 (2001)

    Article  MATH  MathSciNet  Google Scholar 

  18. Inostroza-Ponta, M.: An integrated and scalable approach based on combinatorial optimization techniques for the analysis of microarray data. NOVA | The University of Newcastle’s Digital Repository (2008)

    Google Scholar 

  19. Inostroza-Ponta, M., Berretta, R., Mendes, A., Moscato, P.: An automatic graph layout procedure to visualize correlated data. In: Bramer, M. (ed.) Artificial Intelligence in Theory and Practice. IFIP, vol. 217, pp. 179–188. Springer, Heidelberg (2006)

    Google Scholar 

  20. Inostroza-Ponta, M., Berretta, R., Moscato, P.: QAPgrid: A two level QAP-based approach for large-scale data analysis and visualization. PloS One 6(1), e14468 (2011)

    Google Scholar 

  21. Inostroza-Ponta, M., Mendes, A., Berretta, R., Moscato, P.: An integrated QAP-based approach to visualize patterns of gene expression similarity. In: Randall, M., Abbass, H.A., Wiles, J. (eds.) ACAL 2007. LNCS (LNAI), vol. 4828, pp. 156–167. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  22. Jain, A.K., Murty, M.N., Flynn, P.J.: Data clustering: a review. ACM Computing Surveys (CSUR) 31(3), 264–323 (1999)

    Article  Google Scholar 

  23. Knuth, D.E.: The Art of Computer Programming. Seminumerical algorithms, vol. 2, pp. 229–279. Addison-Wesley, Reading (1969)

    MATH  Google Scholar 

  24. Mahata, P., Costa, W., Cotta, C., Moscato, P.: Hierarchical clustering, languages and cancer. In: Rothlauf, F. (ed.) EvoWorkshops 2006. LNCS, vol. 3907, pp. 67–78. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  25. Marsden, J., Budden, D., Craig, H., Moscato, P.: Language individuation and marker words: Shakespeare and his Maxwell’s demon. PloS One 8(6), e66813 (2013)

    Google Scholar 

  26. Menndez, M., Pardo, J., Pardo, L., Pardo, M.: The Jensen-Shannon divergence. Journal of the Franklin Institute 334(2), 307–318 (1997)

    Article  MathSciNet  Google Scholar 

  27. Ngomo, A.-C.N.: Clique-based clustering. Evaluation 1, 10 (2006)

    Google Scholar 

  28. Rosso, O.A., Craig, H., Moscato, P.: Shakespeare and other english renaissance authors as characterized by information theory complexity quantifiers. Physica A: Statistical Mechanics and its Applications 388(6), 916–926 (2009)

    Article  Google Scholar 

  29. Schmidt, M.C., Samatova, N.F., Thomas, K., Park, B.-H.: A scalable, parallel algorithm for maximal clique enumeration. J. Parallel Distrib. Comput. 69(4), 417–428 (2009)

    Article  Google Scholar 

  30. Sharan, R., Maron-Katz, A., Shamir, R.: CLICK and EXPANDER: A system for clustering and visualizing gene expression data. Bioinformatics 19(14), 1787–1799 (2003)

    Article  Google Scholar 

  31. Zemel, R.S., Carreira-Perpin, M.A.: Proximity graphs for clustering and manifold learning, pp. 225–232. MIT Press (2005)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. The Priority Research Centre for Bioinformatics Biomarker Discovery and Information-based Medicine, Information-based Medicine Program, Hunter Medical Research Institute, School of Electrical Engineering and Computer Science, The University of Newcastle, Australia

    Ahmed Shamsul Arefin, Carlos Riveros, Regina Berretta & Pablo Moscato

Authors
  1. Ahmed Shamsul Arefin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Carlos Riveros
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Regina Berretta
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Pablo Moscato
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. School of Electrical Engineering and Computer Science, The University of Newcastle, 2308, Callaghan, NSW, Australia

    Stephan K. Chalup

  2. School of Computer Science and Engineering, The University of New South Wales, 2052, Sydney, NSW, Australia

    Alan D. Blair

  3. Faculty of Business, Bond University, 4226, Robina, QLD, Australia

    Marcus Randall

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Arefin, A.S., Riveros, C., Berretta, R., Moscato, P. (2015). The MST-kNN with Paracliques. In: Chalup, S.K., Blair, A.D., Randall, M. (eds) Artificial Life and Computational Intelligence. ACALCI 2015. Lecture Notes in Computer Science(), vol 8955. Springer, Cham. https://doi.org/10.1007/978-3-319-14803-8_29

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-14803-8_29

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-14802-1

  • Online ISBN: 978-3-319-14803-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation