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Dominant Set Based Density Kernel and Clustering

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Advances in Neural Networks - ISNN 2017 (ISNN 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10261))

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Abstract

The density peak based clustering algorithm has been shown to be a potential clustering approach. The key of this approach is to isolate and identify cluster centers by estimating the local density of data appropriately. However, existing density kernels are usually dependent on user-specified parameters evidently. In order to eliminate the parameter dependence, in this paper we study the definition of dominant set, which is a graph-theoretic concept of a cluster. As a result, we find that the weights of data in a dominant set provides a non-parametric measure of data density. Based on this observation, we then present an algorithm to estimate data density without parameter input. Experiments on various datasets and comparison with other density kernels demonstrate the effectiveness of our algorithm.

J. Hou—This work is supported in part by the National Natural Science Foundation of China under Grant No. 61473045 and by China Scholarship Council.

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References

  1. Brendan, J.F., Delbert, D.: Clustering by passing messages between data points. Science 315, 972–976 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  2. Bulo, S.R., Pelillo, M., Bomze, I.M.: Graph-based quadratic optimization: a fast evolutionary approach. Comput. Vis. Image Underst. 115(7), 984–995 (2011)

    Article  Google Scholar 

  3. Chang, H., Yeung, D.Y.: Robust path-based spectral clustering. Pattern Recognit. 41(1), 191–203 (2008)

    Article  MATH  Google Scholar 

  4. Daszykowski, M., Walczak, B., Massart, D.L.: Looking for natural patterns in data: Part 1. Density-based approach. Chemom. Intell. Lab. Syst. 56(2), 83–92 (2001)

    Article  Google Scholar 

  5. Ester, M., Kriegel, H.P., Sander, J., Xu, X.W.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: International Conference on Knowledge Discovery and Data Mining, pp. 226–231 (1996)

    Google Scholar 

  6. Gionis, A., Mannila, H., Tsaparas, P.: Clustering aggregation. ACM Trans. Knowl. Discov. Data 1(1), 1–30 (2007)

    Article  Google Scholar 

  7. Hou, J., Gao, H., Li, X.: DSets-DBSCAN: a parameter-free clustering algorithm. IEEE Trans. Image Process. 25(7), 3182–3193 (2016)

    Article  MathSciNet  Google Scholar 

  8. Hou, J., Liu, W., Xu, E., Cui, H.: Towards parameter-independent data clustering. Pattern Recognit. 60, 25–36 (2016)

    Article  Google Scholar 

  9. Jain, A.K.: Data clustering: user’s dilemma. In: Perner, P. (ed.) MLDM 2007. LNCS, vol. 4571, pp. 1–1. Springer, Heidelberg (2007). doi:10.1007/978-3-540-73499-4_1

    Chapter  Google Scholar 

  10. Pavan, M., Pelillo, M.: Dominant sets and pairwise clustering. IEEE Trans. Pattern Anal. Mach. Intell. 29(1), 167–172 (2007)

    Article  Google Scholar 

  11. Rodriguez, A., Laio, A.: Clustering by fast search and find of density peaks. Science 344, 1492–1496 (2014)

    Article  Google Scholar 

  12. Shi, J., Malik, J.: Normalized cuts and image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 22(8), 167–172 (2000)

    Google Scholar 

  13. Veenman, C.J., Reinders, M., Backer, E.: A maximum variance cluster algorithm. IEEE Trans. Pattern Anal. Mach. Intell. 24(9), 1273–1280 (2002)

    Article  Google Scholar 

  14. Yin, S., Gao, H., Qiu, J., Kaynak, O.: Descriptor reduced-order sliding mode observers design for switched systems with sensor and actuator faults. Automatica 76, 282–292 (2017)

    Article  MathSciNet  MATH  Google Scholar 

  15. Yin, S., Gao, H., Qiu, J., Kaynak, O.: Fault detection for nonlinear process with deterministic disturbances: a just-in-time learning based data driven method. IEEE Trans. Cybern. (2016). doi:10.1109/TCYB.2016.2574754

  16. Zahn, C.T.: Graph-theoretical methods for detecting and describing gestalt clusters. IEEE Trans. Comput. 20(1), 68–86 (1971)

    Article  MATH  Google Scholar 

  17. Zhu, X., Loy, C.C., Gong, S.: Constructing robust affinity graphs for spectral clustering. In: IEEE International Conference on Computer Vision and Pattern Recognition, pp. 1450–1457 (2014)

    Google Scholar 

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Authors and Affiliations

  1. College of Engineering, Bohai University, Jinzhou, 121013, China

    Jian Hou

  2. ECLT, Università Ca’ Foscari Venezia, 30124, Venezia, Italy

    Jian Hou

  3. Research Institute of Intelligent Control and Systems, Harbin Institute of Technology, Harbin, 150001, China

    Shen Yin

Authors
  1. Jian Hou
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  2. Shen Yin
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Corresponding author

Correspondence to Jian Hou .

Editor information

Editors and Affiliations

  1. Dalian University of Technology, Dalian, China

    Fengyu Cong

  2. City University of Hong Kong, Kowloon Tong, Hong Kong

    Andrew Leung

  3. Chinese Academy of Sciences, Beijing, China

    Qinglai Wei

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Hou, J., Yin, S. (2017). Dominant Set Based Density Kernel and Clustering. In: Cong, F., Leung, A., Wei, Q. (eds) Advances in Neural Networks - ISNN 2017. ISNN 2017. Lecture Notes in Computer Science(), vol 10261. Springer, Cham. https://doi.org/10.1007/978-3-319-59072-1_11

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  • DOI: https://doi.org/10.1007/978-3-319-59072-1_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-59071-4

  • Online ISBN: 978-3-319-59072-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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