Skip to main content
SpringerLink
Log in

An Evaluation of the Objective Clustering Inductive Technology Effectiveness Implemented Using Density-Based and Agglomerative Hierarchical Clustering Algorithms

  • Conference paper
  • First Online:
Lecture Notes in Computational Intelligence and Decision Making (ISDMCI 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1020))

Abstract

The paper presents the results of the research concerning comparison analysis of the efectiveness of OPTICS and DBSCAN density-based and agglonarative hierarchical clustering algorithms within the framework of the objective clustering inductive technology. Implementation of this technology allows us to determine the optimal parameters of appropriate clustering algorithm in terms of the maximum values of the complex balance criterion which contains as the components both the internal and the external clustering quality criteria. The data from the Computing School of East Finland University database were used as the experimental one during the simulation process. The results of the simulation have shown high effectiveness of the proposed technique. The investigated objects were divided into clusters correctly in all cases. Moreover, the results of the simulation have shown also higher effectiveness of the density-based clustering algorithms in comparison with agglomerative hierarchical algorithm use due to more level of the detail during the objects clustering.

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

Access this chapter

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 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
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

Institutional subscriptions

References

  1. Li C, Liu L, Sun X, Zhao J, Yin J (2019) Image segmentation based on fuzzy clustering with cellular automata and features weighting. EURASIP J Image Video Process 2019(1), article no 37. https://doi.org/10.1186/s13640-019-0436-5

  2. Bi Y, Wang P, Guo X, Wang Z, Cheng S (2019) K-means clustering optimizing deep stacked sparse autoencoder. Sens Imaging 20(1), article no 6. https://doi.org/10.1007/s11220-019-0227-1

  3. Chang Y-S, Yoon SH, Kim JR, Baek S-Y, Cho YS, Hong SH, Kim S, Moon IJ (2019) Standard audiograms for koreans derived through hierarchical clustering using data from the Korean national health and nutrition examination survey 2009–2012. Sci Rep 9(1), article no 3675. https://doi.org/10.1038/s41598-019-40300-7

  4. Wan R, Xiong N, Hu Q, Wang H, Shang J (2019) Similarity-aware data aggregation using fuzzy c-means approach for wireless sensor networks. EURASIP J Wirel Commun Netw 2019(1), article no 59. https://doi.org/10.1186/s13638-019-1374-8

  5. Khatoon M, Banu WA (2019) An efficient method to detect communities in social networks using DBSCAN algorithm. Soc Netw Anal Min 9(1), article no 9. https://doi.org/10.1007/s13278-019-0554-1

  6. Gómez SLS, Rodríguez JDS, Rodríguez FJI, Juez FJC (2017) Analysis of the temporal structure evolution of physical systems with the self-organising tree algorithm (SOTA): application for validating neural network systems on adaptive optics data before on-sky implementation. Entropy 19(3), article no 103. https://doi.org/10.3390/e19030103

    Article  Google Scholar 

  7. Ros F, Guillaume S (2019) A hierarchical clustering algorithm and an improvement of the single linkage criterion to deal with noise. Expert Syst Appl 128:96–108. https://doi.org/10.1016/j.eswa.2019.03.031

    Article  Google Scholar 

  8. Frid A, Manevitz LM, Mosafi O (2019) Kohonen-based topological clustering as an amplifier for multi-class classification for Parkinson’s disease. In: 2018 IEEE international conference on the science of electrical engineering in Israel, ICSEE 2018, article no 8646026. https://doi.org/10.1109/ICSEE.2018.8646026

  9. Silva EDS, da Silva EGP, Silva DDS, Novaes CG, Amorim FAC, dos Santos MJS, Bezerra MA (2019) Evaluation of macro and micronutrient elements content from soft drinks using principal component analysis and Kohonen self-organizing maps. Food Chem 273:9–14. https://doi.org/10.1016/j.foodchem.2018.06.021

    Article  Google Scholar 

  10. Tkachenko R, Izonin I (2019) Model and principles for the implementation of neural-like structures based on geometric data transformations. Adv Intell Syst Comput 754:578–587. https://doi.org/10.1007/978-3-319-91008-6_58

    Article  Google Scholar 

  11. Vitynskyi P, Tkachenko R, Izonin I, Kutucu H (2018) Hybridization of the SGTM neural-like structure through inputs polynomial extension. In: Proceedings of the 2018 IEEE 2nd international conference on data stream mining and processing, DSMP 2018, article no 8478456, pp 386–391. https://doi.org/10.1109/DSMP.2018.8478456

  12. Compute clustering validation indices. https://cran.r-project.org/web/packages/clusterCrit/clusterCrit.pdf

  13. Ihaka R, Gentleman R (1996) R: a linguage for data analysis and graphics. J Comput Graph Stat 5(3):299–314

    Google Scholar 

  14. Babichev S, Taif MA, Lytvynenko V, Osypenko V (2017) Criterial analysis of gene expression sequences to create the objective clustering inductive technology. In: Proceedings of 2017 IEEE 37th international conference on electronics and nanotechnology, ELNANO 2017, article no. 7939756, pp 244–248. https://doi.org/10.1109/ELNANO.2017.7939756

  15. Calinski T, Harabasz J (1974) A dendrite method for cluster analysis. Commun Stat 3:1–27

    MathSciNet  MATH  Google Scholar 

  16. Zhao Q, Xu M, Fränti P (2009) Sum-of-squares based cluster validity index and significance analysis. Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics), vol 5495, pp 313–322. https://doi.org/10.1007/978-3-642-04921-7_32

    Chapter  Google Scholar 

  17. Madala HR, Ivakhnenko AG (1994) Inductive learning algorithms for complex systems modeling. CRC Press, Boca Raton

    MATH  Google Scholar 

  18. Ivakhnenko AG (1987) Objective clustering based on the theory of self-organization models. Automatics 5:6–15 (in Russian)

    MathSciNet  Google Scholar 

  19. Stepashko V (2017) Inductive modeling from historical perspective. In: Proceedings of the 2017 12th international scientific and technical conference on computer sciences and information technologies, CSIT 2017, vol 1, article no 8098845, pp 537–542. https://doi.org/10.1109/STC-CSIT.2017.8098845

  20. Yefimenko S, Stepashko V (2015) Intelligent recurrent-and-parallel computing for solving inductive modeling problems. In: Proceedings - 2015 16th international conference on computational problems of electrical engineering, CPEE 2015, article no 7333385, pp 236–238. https://doi.org/10.1109/CPEE.2015.7333385

  21. Babichev S, Lytvynenko V, Korobchynskyi M, Taiff MA (2017) Objective clustering inductive technology of gene expression sequences features. Commun Comput Inf Sci 716:359–372. https://doi.org/10.1007/978-3-319-58274-0_29

    Article  Google Scholar 

  22. Babichev S, Taif MA, Lytvynenko V (2016) Estimation of the inductive model of objects clustering stability based on the k-means algorithm for different levels of data noise. Radio Electron Comput Sci Control 4(4):54–60

    Google Scholar 

  23. Babichev S, Taif MA, Lytvynenko V (2016) Inductive model of data clustering based on the agglomerative hierarchical algorithm. In: Proceedings of the 2016 IEEE 1st international conference on data stream mining and processing, DSMP 2016, article no 7583499, pp 19–22. https://doi.org/10.1109/DSMP.2016.7583499

  24. Babichev S, Lytvynenko V, Skvor J, Fiser J (2018) Model of the objective clustering inductive technology of gene expression profiles based on SOTA and DBSCAN clustering algorithms. Adv Intellt Syst Comput 689:21–39. https://doi.org/10.1007/978-3-319-70581-1_2

    Article  Google Scholar 

  25. Babichev S, Lytvynenko V, Skvor J, Korobchynskyi M, Voronenko M (2018) Information technology of gene expression profiles processing for purpose of gene regulatory networks reconstruction. In: Proceedings of the 2018 IEEE 2nd international conference on data stream mining and processing, DSMP 2018, article no 8478452, pp 336–341. https://doi.org/10.1109/DSMP.2018.8478452

  26. Babichev S, Korobchynskyi M, Mieshkov S, Korchomnyi O (2018) An effectiveness evaluation of information technology of gene expression profiles processing for gene networks reconstruction. Int J Intell Syst Appl 10(7):1–10. https://doi.org/10.5815/ijisa.2018.07.01

    Article  Google Scholar 

  27. Fefelov AO, Lytvynenko VI, Taif MA, Savina NB, Voronenko MA, Lurie IA, Boskin OO (2019) Hybrid immune algorithms in the gene regulatory networks reconstruction. In: CEUR Workshop Proceedings, vol 2353, pp 193–210

    Google Scholar 

  28. Harrington J (1965) The desirability function. Ind Qual Control 21(10):494–498

    Google Scholar 

  29. Ankerst M, Breunig MM, Kriegel H-P, Sander J (1999) OPTICS: ordering points to identify the clustering structure. In: ACM special interest group on management of data record SIGMOD, vol 28, no 2, pp 49–60. https://doi.org/10.1145/304181.304187

    Article  Google Scholar 

  30. Ester M, Kriegel HP, Sander J, Xu X (1996) A density-based algorithm for discovering clusters in large spatial datasets with noise. In: Proceedings of the second international conference on knowledge discovery and data mining, Portland, Oregon, pp 226–231

    Google Scholar 

  31. Density Based Clustering of Applications with Noise (DBSCAN) and Related Algorithms. https://cran.r-project.org/web/packages/dbscan/dbscan.pdf

  32. Maechler M et al: “Finding groups in data”: cluster analysis extended Rousseeuw et al. https://cran.r-project.org/web/packages/cluster/index.html

  33. Nguyen T-D, Schmidt B, Kwoh C-K (2014) SparseHC: a memory-efficient online hierarchical clustering algorithm. Procedia Comput Sci 29:8–19. https://doi.org/10.1016/j.procs.2014.05.001

    Article  Google Scholar 

  34. Gionis A, Mannila H, Tsaparas P (2007) Clustering aggregation. ACM Trans Knowl Disc Data 1(1), article no 1217303. https://doi.org/10.1145/1217299.1217303

    Article  Google Scholar 

  35. Zahn CT (1971) Graph-theoretical methods for detecting and describing gestalt clusters. IEEE Trans Comput C–20(1):68–86. https://doi.org/10.1109/T-C.1971.223083

    Article  MATH  Google Scholar 

  36. Factoextra : Extract and Visualize the Results of Multivariate Data Analyses. https://rpkgs.datanovia.com/factoextra/index.html

  37. Jain AK, Law MHC (2005) Data clustering: a user’s dilemma. Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics), vol 3776, pp 1–10

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Jan Evangelista Purkyne University in Usti nad Labem, Usti nad Labem, Czech Republic

    Sergii Babichev

  2. Ukrainian Academy of Printing, Lviv, Ukraine

    Sergii Babichev, Bohdan Durnyak, Iryna Pikh & Vsevolod Senkivskyy

Authors
  1. Sergii Babichev
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Bohdan Durnyak
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Iryna Pikh
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Vsevolod Senkivskyy
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sergii Babichev .

Editor information

Editors and Affiliations

  1. Kherson National Technical University, Kherson, Ukraine

    Volodymyr Lytvynenko

  2. Jan Evangelista Purkyně University in Ústi and Labem, Usti and Labem, Czech Republic

    Sergii Babichev

  3. Lublin University of Technology, Lublin, Poland

    Waldemar Wójcik

  4. IT Step University, Lviv, Ukraine

    Olena Vynokurova

  5. Kherson National Technical University, Kherson, Ukraine

    Svetlana Vyshemyrskaya

  6. Kherson National Technical University, Kherson, Ukraine

    Svetlana Radetskaya

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Babichev, S., Durnyak, B., Pikh, I., Senkivskyy, V. (2020). An Evaluation of the Objective Clustering Inductive Technology Effectiveness Implemented Using Density-Based and Agglomerative Hierarchical Clustering Algorithms. In: Lytvynenko, V., Babichev, S., Wójcik, W., Vynokurova, O., Vyshemyrskaya, S., Radetskaya, S. (eds) Lecture Notes in Computational Intelligence and Decision Making. ISDMCI 2019. Advances in Intelligent Systems and Computing, vol 1020. Springer, Cham. https://doi.org/10.1007/978-3-030-26474-1_37

Download citation

Publish with us

Policies and ethics

Search

Navigation