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Grid-Based Approach to Determining Parameters of the DBSCAN Algorithm

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Artificial Intelligence and Soft Computing (ICAISC 2020)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12415))

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Abstract

Clustering is a very important technique used in many fields in order to deal with large datasets. In clustering algorithms, one of the most popular approaches is based on an analysis of clusters density. Density-based algorithms include different methods but the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is one of the most cited in the scientific literature. This algorithm can identify clusters of arbitrary shapes and sizes that occur in a dataset. Thus, the DBSCAN is very widely applied in various applications and has many modifications. However, there is a key issue of the right choice of its two input parameters, i.e the neighborhood radius (eps) and the MinPts. In this paper, a new method for determining the neighborhood radius (eps) and the MinPts is proposed. This method is based on finding a proper grid of cells for a dataset. Next, the grid is used to calculate the right values of these two parameters. Experimental results have been obtained for several different datasets and they confirm a very good performance of the newly proposed method.

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References

  1. Bilski, J., Smoląg, J., Żurada, J.M.: Parallel approach to the Levenberg-Marquardt learning algorithm for feedforward neural networks. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2015. LNCS (LNAI), vol. 9119, pp. 3–14. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-19324-3_1

    Chapter  MATH  Google Scholar 

  2. Bilski, J., Wilamowski, B.M.: Parallel Levenberg-Marquardt algorithm without error backpropagation. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2017. LNCS (LNAI), vol. 10245, pp. 25–39. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-59063-9_3

    Chapter  Google Scholar 

  3. Bradley, P., Fayyad, U.: Refining initial points for k-means clustering. In: Proceedings of the Fifteenth International Conference on Knowledge Discovery and Data Mining, pp. 9–15. AAAI Press, New York (1998)

    Google Scholar 

  4. Bologna, G., Hayashi, Y.: Characterization of symbolic rules embedded in deep DIMLP networks: a challenge to transparency of deep learning. J. Artif. Intell. Soft Comput. Res. 7(4), 265–286 (2017)

    Article  Google Scholar 

  5. Chen, X., Liu, W., Qui, H., Lai, J.: APSCAN: a parameter free algorithm for clustering. Pattern Recogn. Lett. 32, 973–986 (2011)

    Article  Google Scholar 

  6. Chen, J.: Hybrid clustering algorithm based on PSO with the multidimensional asynchronism and stochastic disturbance method. J. Theor. Appl. Inform. Technol. 46, 343–440 (2012)

    Article  Google Scholar 

  7. Chen, Y., Tang, S., Bouguila, N., Wang, C., Du, J., Li, H.: A fast clustering algorithm based on pruning unnecessary distance computations in DBSCAN for high-dimensional data. Pattern Recogn. 83, 375–387 (2018)

    Article  Google Scholar 

  8. D’Aniello, G., Gaeta, M., Loia, F., Reformat, M., Toti, D.: An environment for collective perception based on fuzzy and semantic approaches. J. Artif. Intell. Soft Comput. Res. 8(3), 191–210 (2018)

    Article  Google Scholar 

  9. Ester, M., Kriegel, H.P., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: Proceeding of 2nd International Conference on Knowledge Discovery and Data Mining, pp. 226–231 (1996)

    Google Scholar 

  10. Fränti, P., Rezaei, M., Zhao, Q.: Centroid index: cluster level similarity measure. Pattern Recogn. 47(9), 3034–3045 (2014)

    Article  Google Scholar 

  11. Hruschka, E.R., de Castro, L.N., Campello, R.J.: Evolutionary algorithms for clustering gene-expression data. In: Data Mining, Fourth IEEE International Conference on Data Mining (ICDM 2004), pp. 403–406. IEEE (2004)

    Google Scholar 

  12. Karami, A., Johansson, R.: Choosing DBSCAN parameters automatically using differential evolution. Int. J. Comput. Appl. 91, 1–11 (2014)

    Google Scholar 

  13. Lai, W., Zhou, M., Hu, F., Bian, K., Song, Q.: A new DBSCAN parameters determination method based on improved MVO. IEEE Access 7, 104085–104095 (2019)

    Article  Google Scholar 

  14. Liu, H., Gegov, A., Cocea, M.: Rule based networks: an efficient and interpretable representation of computational models. J. Artif. Intell. Soft Comput. Res. 7(2), 111–123 (2017)

    Article  Google Scholar 

  15. Luchi, D., Rodrigues, A.L., Varejao, F.M.: Sampling approaches for applying DBSCAN to large datasets. Pattern Recogn. Lett. 117, 90–96 (2019)

    Article  Google Scholar 

  16. Ferdaus, M.M., Anavatti, S.G., Matthew, A., Pratama, G., Pratama, M.: Development of C-means clustering based adaptive fuzzy controller for a flapping wing micro air vehicle. J. Artif. Intell. Soft Comput. Res. 9(2), 99–109 (2019). https://doi.org/10.2478/jaiscr-2018-0027

    Article  Google Scholar 

  17. Murtagh, F.: A survey of recent advances in hierarchical clustering algorithms. Comput. J. 26(4), 354–359 (1983)

    Article  Google Scholar 

  18. Patrikainen, A., Meila, M.: Comparing subspace clusterings. IEEE Trans. Knowl. Data Eng. 18(7), 902–916 (2006)

    Article  Google Scholar 

  19. Pei, Z., Hua, X., Han, J.: The clustering algorithm based on particle swarm optimization algorithm. In: Proceedings of the 2008 International Conference on Intelligent Computation Technology and Automation, Washington, USA, vol. 1, pp. 148–151 (2008)

    Google Scholar 

  20. Prasad, M., Liu, Y.-T., Li, D.-L., Lin, C.-T., Shah, R.R., Kaiwartya, O.P.: A new mechanism for data visualization with TSK-type preprocessed collaborative fuzzy rule based system. J. Artif. Intell. Soft Comput. Res. 7(1), 33–46 (2017)

    Article  Google Scholar 

  21. Rastin, P., Matei, B., Cabanes, G., Grozavu, N., Bennani, Y.: Impact of learners’ quality and diversity in collaborative clustering. J. Artif. Intell. Soft Comput. Res. 9(2), 149–165 (2019). https://doi.org/10.2478/jaiscr-2018-0030

    Article  Google Scholar 

  22. Riid, A., Preden, J.-S.: Design of fuzzy rule-based classifiers through granulation and consolidation. J. Artif. Intell. Soft Comput. Res. 7(2), 137–147 (2017)

    Article  Google Scholar 

  23. Rohlf, F.: Single-link clustering algorithms. In: Krishnaiah, P.R., Kanal, L.N., (eds.) Handbook of Statistics, vol. 2, pp. 267–284 (1982)

    Google Scholar 

  24. Rutkowski, T., Łapa, K., Nielek, R.: On explainable fuzzy recommenders and their performance evaluation. Int. J. Appl. Math. Comput. Sci. 29(3), 595–610 (2019). https://doi.org/10.2478/amcs-2019-0044

    Article  MATH  Google Scholar 

  25. Rutkowski, T., Łapa, K., Jaworski, M., Nielek, R., Rutkowska, D.: On explainable flexible fuzzy recommender and its performance evaluation using the akaike information criterion. In: Gedeon, T., Wong, K.W., Lee, M. (eds.) ICONIP 2019. CCIS, vol. 1142, pp. 717–724. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-36808-1_78

    Chapter  Google Scholar 

  26. Sameh, A.S., Asoke, K.N.: Development of assessment criteria for clustering algorithms. Pattern Anal. Appl. 12(1), 79–98 (2009)

    Article  MathSciNet  Google Scholar 

  27. Shah G.H.: An improved DBSCAN, a density based clustering algorithm with parameter selection for high dimensional data sets. In: Nirma University International Engineering (NUiCONE), pp. 1–6 (2012)

    Google Scholar 

  28. Sheikholeslam, G., Chatterjee, S., Zhang, A.: WaveCluster: a wavelet-based clustering approach for spatial data in very large databases. Int. J. Very Large Data Bases 8(3–4), 289–304 (2000)

    Article  Google Scholar 

  29. Shieh, H.-L.: Robust validity index for a modified subtractive clustering algorithm. Appl. Soft Comput. 22, 47–59 (2014)

    Article  Google Scholar 

  30. Starczewski, A.: A new validity index for crisp clusters. Pattern Anal. Appl. 20(3), 687–700 (2017)

    Article  MathSciNet  Google Scholar 

  31. Wang, W., Yang, J., Muntz, R.: STING: a statistical information grid approach to spatial data mining. In: Proceedings of the 23rd International Conference on Very Large Data Bases. (VLDB 1997), pp. 186–195 (1997)

    Google Scholar 

  32. Zalik, K.R.: An efficient k-means clustering algorithm. Pattern Recogn. Lett. 29(9), 1385–1391 (2008)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Institute of Computational Intelligence, Częstochowa University of Technology, Al. Armii Krajowej 36, 42-200, Częstochowa, Poland

    Artur Starczewski

  2. Information Technology Institute, University of Social Sciences, 90-113, Łódź, Poland

    Andrzej Cader

  3. Clark University, Worcester, MA, 01610, USA

    Andrzej Cader

Authors
  1. Artur Starczewski
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  2. Andrzej Cader
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Corresponding author

Correspondence to Artur Starczewski .

Editor information

Editors and Affiliations

  1. Częstochowa University of Technology, Częstochowa, Poland

    Leszek Rutkowski

  2. Częstochowa University of Technology, Częstochowa, Poland

    Rafał Scherer

  3. Częstochowa University of Technology, Częstochowa, Poland

    Marcin Korytkowski

  4. Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada

    Witold Pedrycz

  5. AGH University of Science and Technology, Kraków, Poland

    Ryszard Tadeusiewicz

  6. Electrical and Computer Engineering, University of Louisville, Louisville, KY, USA

    Jacek M. Zurada

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Starczewski, A., Cader, A. (2020). Grid-Based Approach to Determining Parameters of the DBSCAN Algorithm. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2020. Lecture Notes in Computer Science(), vol 12415. Springer, Cham. https://doi.org/10.1007/978-3-030-61401-0_52

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  • DOI: https://doi.org/10.1007/978-3-030-61401-0_52

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  • Online ISBN: 978-3-030-61401-0

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