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A Semi-supervised Fuzzy Clustering Approach via Modifications of the DBSCAN Algorithm

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10th International Conference on Theory and Application of Soft Computing, Computing with Words and Perceptions - ICSCCW-2019 (ICSCCW 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1095))

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Abstract

In the data mining context, semi-supervised learning is applicable in circumstances where only a scarce amount of information on the intrinsic structure of a dataset is available. This information may be in the form a few labelled instances or a relatively small set of constraints on the pairwise memberships of particular instances. In this study we are providing a semi-supervised fuzzy clustering model which modifies versions of conventional DBSCAN algorithm in order to generate soft clusters which foreclose the noise points. The employed modifications are mostly related to the control parameters of the algorithm intending to utilize the additional information (which in our case is in the form of a few labelled instances) and adaptations towards the fuzzy clustering approach. Finally, several experimental procedures have been conducted on synthetic and real-world benchmark datasets in order to assess the accuracy of our employed model and to compare it to the conventional algorithms of the respective domain.

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References

  1. Zhu, X.: Semi-supervised learning literature survey. University of Wisconsin-Madison, Department of Computer Sciences (2005)

    Google Scholar 

  2. Bedalli, E., Mançellari, E., Asilkan, O.: A heterogeneous cluster ensemble model for improving the stability of fuzzy cluster analysis. Proc. Comput. Sci. 102, 129–136.4 (2016)

    Article  Google Scholar 

  3. Berkhin, P.: A survey of clustering data mining techniques. In: Grouping Multidimensional Data, pp. 25–71. Springer, Heidelberg (2006)

    Google Scholar 

  4. Bedalli, E., Ninka, I.: Adapting the fuzzy c-means clustering algorithm for a semi-supervised learning approach. Sci. Innov. New Technol. 1, 61 (2014)

    Google Scholar 

  5. Grabocka, J., Bedalli, E., Schmidt-Thieme, L.: Supervised nonlinear factorizations excel in semi-supervised regression. In: Pacific-Asia Conference on Knowledge Discovery and Data Mining, pp. 188–199. Springer, Cham (2014)

    Chapter  Google Scholar 

  6. Schubert, E., Sander, J., Ester, M., Kriegel, H.-P., Xu, X.: DBSCAN revisited, revisited: why and how you should (still) use DBSCAN. ACM Trans. Database Syst. (TODS) 42(3), 19 (2017)

    Article  MathSciNet  Google Scholar 

  7. Wagstaff, K., Cardie, C., Rogers, S., Schrödl, S.: Constrained k-means clustering with background knowledge. In: ICML, vol. 1, pp. 577–584 (2001)

    Google Scholar 

  8. Grira, N., Crucianu, M., Boujemaa, N.: Semi-supervised fuzzy clustering with pairwise-constrained competitive agglomeration. In: The 14th IEEE International Conference on Fuzzy Systems. IEEE (2005)

    Google Scholar 

  9. Lelis, L., Sander, J.: Semi-supervised density-based clustering. In: Ninth IEEE International Conference on Data Mining. IEEE (2009)

    Google Scholar 

  10. Abir, S., Eloudi, Z.: Soft DBSCAN: improving DBSCAN clustering method using fuzzy set theory. In: 2013 6th International Conference on Human System Interactions (HSI), pp. 380–385. IEEE (2013)

    Google Scholar 

  11. Gan, J., Tao, Y.: DBSCAN revisited: mis-claim, un-fixability, and approximation. In: Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data, pp. 519–530 (2015)

    Google Scholar 

  12. Bedalli, E., Enea, M., Esteriana, H.: Exploring user feedback data via a hybrid fuzzy clustering model combining variations of FCM and density-based clustering. In: International Conference on Intelligent Networking and Collaborative Systems, pp. 71–81. Springer, Cham (2018)

    Google Scholar 

  13. Khan, K., Rehman, S.U., Aziz, K., Fong, S., Sarasvady, S.: DBSCAN: past, present and future. In: The Fifth International Conference on the Applications of Digital Information and Web Technologies (ICADIWT 2014), pp. 232–238 (2014)

    Google Scholar 

  14. Amin, K., Johansson, R.: Choosing DBSCAN parameters automatically using differential evolution. Int. J. Comput. Appl. 91(7), 1–11 (2014)

    Google Scholar 

  15. Carlos, R., Spiliopoulou, M., Menasalvas, E.: C-DBSCAN: density-based clustering with constraints. In: International Workshop on Rough Sets, Fuzzy Sets, Data Mining, and Granular-Soft Computing, pp. 216–223. Springer, Heidelberg, (2007)

    Google Scholar 

  16. Dino, I., Bordogna, G.: Fuzzy extensions of the DBSCAN clustering algorithm. Soft Comput. 22(5), 1719–1730 (2018)

    Article  Google Scholar 

  17. Arthur, A., Newman, D.: UCI machine learning repository (2007)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Epoka University, Tirana, Albania

    Erind Bedalli & Enea Mançellari

  2. University of Elbasan, Elbasan, Albania

    Erind Bedalli & Rexhep Rada

Authors
  1. Erind Bedalli
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  2. Enea Mançellari
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  3. Rexhep Rada
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Corresponding author

Correspondence to Erind Bedalli .

Editor information

Editors and Affiliations

  1. Joint MBA Program, Azerbaijan State Oil and Industry University, Baku, Azerbaijan

    Rafik A. Aliev

  2. Polish Academy of Sciences, Systems Research Institute, Warsaw, Poland

    Janusz Kacprzyk

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

    Witold Pedrycz

  4. Department of Electrical and Computer Engineering, University of Texas at San Antonio, San Antonio, TX, USA

    Mo Jamshidi

  5. Azerbaijan State Oil and Industry University, Baku, Azerbaijan

    Mustafa B. Babanli

  6. Department of Mechatronics, Near East University, Nicosia, Turkey

    Fahreddin M. Sadikoglu

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Bedalli, E., Mançellari, E., Rada, R. (2020). A Semi-supervised Fuzzy Clustering Approach via Modifications of the DBSCAN Algorithm. In: Aliev, R., Kacprzyk, J., Pedrycz, W., Jamshidi, M., Babanli, M., Sadikoglu, F. (eds) 10th International Conference on Theory and Application of Soft Computing, Computing with Words and Perceptions - ICSCCW-2019. ICSCCW 2019. Advances in Intelligent Systems and Computing, vol 1095. Springer, Cham. https://doi.org/10.1007/978-3-030-35249-3_29

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