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A New Clustering Algorithm Based on Graph Connectivity

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Intelligent Computing (SAI 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 858))

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Abstract

A new clustering algorithm based on the concept of graph connectivity is introduced. The idea is to develop a meaningful graph representation for data, where each resulting sub-graph corresponds to a cluster with highly similar objects connected by edge. The proposed algorithm has a fairly strong theoretical basis that supports its originality and computational efficiency. Further, some useful guidelines are provided so that the algorithm can be tuned to optimize the well-designed quality indices. Numerical evidences show that the proposed algorithm can provide a very good clustering accuracy for a number of benchmark data and has a relatively low computational complexity compared to some sophisticated clustering methods.

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References

  1. Hansen, P., Jaumard, B.: Cluster analysis and mathematical programming. Math. Program. 79, 191–215 (1997)

    MathSciNet  MATH  Google Scholar 

  2. Sneath, P.: The application of computers to taxonomy. J. Gen. Microbiol. 17, 201–226 (1957)

    Article  Google Scholar 

  3. Sorensen, T.: A method of establishing groups of equal amplitude in plant sociology based on similarity of species content and its application to analyzes of the vegetation on danish commons. Biologiske Skrifter 5, 1–34 (1948)

    Google Scholar 

  4. Kaufman, L., Rousseeuw, P.: Finding Groups in Data: An Introduction to Cluster Analysis. Wiley, New York (1990)

    Book  Google Scholar 

  5. Lloyd, S.: Least squares quantization in PCM. IEEE Trans. Inf. Theory 28, 129–137 (1982)

    Article  MathSciNet  Google Scholar 

  6. Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning. Springer, New York (2001)

    Book  Google Scholar 

  7. Jain, A.K., Dubes, R.C.: Algorithms for Clustering Data. Prentice-Hall, New Jersey (1988)

    MATH  Google Scholar 

  8. Everitt, B., Landau, S.: Cluster Analysis. Amold, London (2001)

    MATH  Google Scholar 

  9. Harary, F.: Graph Theory. Addison-Wesley, Boston (1969)

    Book  Google Scholar 

  10. Karypis, G., Han, E., Kumar, V.: Chameleon: hierarchical clustering using dynamic modeling. IEEE Comput. 32, 68–75 (1999)

    Article  Google Scholar 

  11. Zadeh, L.: Fuzzy sets. Inf. Control 8, 338–353 (1965)

    Article  Google Scholar 

  12. Kohonen, T.: Self-organizing Maps. Springer, New York (2001)

    Book  Google Scholar 

  13. Schölkopf, B., Smola, A., Müller, K.: Nonlinear component analysis as a kernel eigenvalue problem. Neural Comput. 10, 1299–1319 (1998)

    Article  Google Scholar 

  14. Matula, D.W.: The cohesive strength of graphs. In: Chartrand, G., Kapoor, S.F. (eds.) The Many Facets of Graph Theory. Lecture Notes in Mathematics, vol. 110, pp. 215–221. Springer, Berlin (1969)

    Chapter  Google Scholar 

  15. Matula, D.W.: k-components, clusters and slicings in graphs. SIAM J. Appl. Math. 22, 459–480 (1972)

    Article  MathSciNet  Google Scholar 

  16. Cherng, J., Lo, M.: A hypergraph based clustering algorithm for spatial data sets. In: Proceedings of the IEEE International Conference on Data Mining, pp. 83–90 (2001)

    Google Scholar 

  17. Estivill-Castro, V., Lee, I.: AMOEBA: hierarchical clustering based on spatial proximity using Delaunay diagram. In: Proceedings of the 9th Symposium on Spatial Data Handling, pp. 7a.26–7a.41 (1999)

    Google Scholar 

  18. Hartuv, E., Shamir, R.: A clustering algorithm based on graph connectivity. Inf. Process. Lett. 76, 175–181 (2000)

    Article  MathSciNet  Google Scholar 

  19. Sharan, R., Shamir, R.: CLICK: a clustering algorithm with applications to gene expression analysis. In: Proceedings of the 8th International Conference on Intelligent Systems for Molecular Biology, pp. 307–316 (2000)

    Google Scholar 

  20. Ben-Dor, A., Shamir, R., Yakhini, Z.: Clustering gene expression patterns. J. Comput. Biol. 6, 281–297 (1999)

    Article  Google Scholar 

  21. Zhou, Y., Cheng, H., Yu, J.X.: Graph clustering based on structural/attribute similarities. Proc. VLDB Endow. 2, 718–729 (2009)

    Article  Google Scholar 

  22. Parimala, M., Lopez, D.: Graph clustering based on structural attribute neighborhood similarity (SANS). In: Proceedings of the IEEE International Conference on Electrical, Computer and Communication Technologies (ICECCT), pp. 1–4 (2015)

    Google Scholar 

  23. Milligan, G., Cooper, M.: An examination of procedures for determining the number of clusters in a data set. Psychometrika 50, 159–179 (1985)

    Article  Google Scholar 

  24. Blum, M., Floyd, R.W., Pratt, V.R., Rivest, R.L., Tarjan, R.E.: The bounds for selection. J. Comput. Syst. Sci. 7, 448–461 (1973)

    Article  MathSciNet  Google Scholar 

  25. Moore, E.F.: The shortest path through a maze. In: Proceedings of the International Symposium on the Theory of Switching, pp. 285–292 (1959)

    Google Scholar 

  26. Stoer, M., Wagner, F.: A simple min-cut algorithm. J. ACM 44, 585–591 (1997)

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electrical Engineering, National Taiwan University, Taipei, 10617, Taiwan

    Yu-Feng Li & Liang-Hung Lu

  2. Department of Statistics, National Chengchi University, Taipei, 11605, Taiwan

    Ying-Chao Hung

Authors
  1. Yu-Feng Li
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  2. Liang-Hung Lu
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  3. Ying-Chao Hung
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Corresponding author

Correspondence to Ying-Chao Hung .

Editor information

Editors and Affiliations

  1. Faculty of Science and Engineering, Department of Information Science, Saga University, Honjo, Saga, Japan

    Kohei Arai

  2. The Science and Information (SAI) Organization, Bradford, West Yorkshire, UK

    Supriya Kapoor

  3. The Science and Information (SAI) Organization, Bradford, West Yorkshire, UK

    Rahul Bhatia

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Li, YF., Lu, LH., Hung, YC. (2019). A New Clustering Algorithm Based on Graph Connectivity. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Intelligent Computing. SAI 2018. Advances in Intelligent Systems and Computing, vol 858. Springer, Cham. https://doi.org/10.1007/978-3-030-01174-1_33

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