Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content

Advertisement

SpringerLink
Log in

Ant colony optimization for mining gradual patterns

  • Original Article
  • Published:
International Journal of Machine Learning and Cybernetics Aims and scope Submit manuscript

Abstract

Gradual pattern extraction is a field in Knowledge Discovery in Databases that maps correlations between attributes of a data set as gradual dependencies. A gradual dependency may take the form: “the more Attribute\(_{K}\), the less Attribute\(_{L}\)”. Classical approa-ches for extracting gradual patterns extend either a breath-first search or a depth-first search strategy. However, these strategies can be computationally expensive and inefficient especially when dealing with large data sets. In this study, we investigate 3 population-based optimization techniques (i.e. ant colony optimization, genetic algorithm and particle swarm optimization) that may be employed improve the efficiency of mining gradual patterns. We show that ant colony optimization technique is better suited for gradual pattern mining task than the other 2 techniques. Through computational experiments on real-world data sets, we compared the computational performance of the proposed algorithms that implement the 3 population-based optimization techniques to classical algorithms for the task of gradual pattern mining and we show that the proposed algorithms outperform their classical counterparts.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

Notes

  1. https://meso-lr.umontpellier.fr.

  2. https://data.oreme.org.

  3. https://meso-lr.umontpellier.fr.

References

  1. Aryadinata YS, Laurent A, Sala M (2013) M2LGP? Mining multiple level gradual patterns. Int J Comput Inf Eng 7(3):353–360

    Google Scholar 

  2. Aryadinata YS, Lin Y, Barcellos C, Laurent A, Libourel T (2014) Mining epidemiological dengue fever data from Brazil: a gradual pattern based geographical information system. Commun Comput Inf Sci 443 CCIS:414–423. https://doi.org/10.1007/978-3-319-08855-6_42

  3. Berzal F, Cubero JC, Sanchez D, Vila MA, Serrano JM (2007) An alternative approach to discover gradual dependencies. Int J Uncertain Fuzziness Knowl Based Syst 15(05):559–570. https://doi.org/10.1142/S021848850700487X

    Article  MathSciNet  MATH  Google Scholar 

  4. Blum C (2005) Ant colony optimization: introduction and recent trends. Phys Life Rev 2(4):353–373. https://doi.org/10.1016/j.plrev.2005.10.001

    Article  Google Scholar 

  5. Bouchette F (2019) OREME: the coastline observation system. https://oreme.org/observation/ltc/. Accessed June 2021

  6. Cicirello VA, Smith SF (2001) Ant colony control for autonomous decentralized shop floor routing. In: Proceedings 5th international symposium on autonomous decentralized systems, pp 383–390. https://doi.org/10.1109/ISADS.2001.917443

  7. Cormen TH, Leiserson CE, Rivest RL, Stein C (2009) Introduction to algorithms. MIT Press, Cambridge

    MATH  Google Scholar 

  8. Di-Jorio L, Laurent A, Teisseire M (2009) Mining frequent gradual itemsets from large databases. In: Advances in intelligent data analysis VIII. Springer, Berlin, pp 297–308. https://doi.org/10.1007/978-3-642-03915-7_26

  9. Dorigo M, Birattari M (2010) Ant colony optimization. Springer US, Boston, pp 36–39. https://doi.org/10.1007/978-0-387-30164-8_22

  10. Dorigo M, Maniezzo V, Colorni A (1996) Ant system: optimization by a colony of cooperating agents. IEEE Trans Syst Man Cybern Part B (Cybern) 26(1):29–41. https://doi.org/10.1109/3477.484436

  11. Dorigo M, Stützle T (2019) Ant colony optimization: overview and recent advances. Springer International Publishing, Cham, pp. 311–351. https://doi.org/10.1007/978-3-319-91086-4_10

  12. Dua D, Graff C (2019) UCI machine learning repository. http://archive.ics.uci.edu/ml. Accessed June 2021

  13. Fiot C, Masseglia F, Laurent A, Teisseire M (2009) Evolution patterns and gradual trends. Int J Intell Syst 24(10):1013–1038. https://doi.org/10.1002/int.20369

    Article  MATH  Google Scholar 

  14. Goldberg DE, Holland JH (1988) Genetic Algorithms and Machine Learning. Mach Learn 3:95–99. https://doi.org/10.1023/A:1022602019183

  15. Hartmann SA, Runkler TA (2008) Online optimization of a color sorting assembly buffer using ant colony optimization. In: Kalcsics J, Nickel S (eds) Operations Research Proceedings 2007. Springer, Berlin, pp 415–420

    Chapter  Google Scholar 

  16. Holland JH (1992) Genetic algorithms. Sci Am 267(1):66–73

    Article  Google Scholar 

  17. Jünger M, Reinelt G, Rinaldi G (1995) Chapter 4 the traveling salesman problem. In: Network models, handbooks in operations research and management science, vol 7, pp 225–330. Elsevier. https://doi.org/10.1016/S0927-0507(05)80121-5

  18. Kabir MMJ, Xu S, Kang BH, Zhao Z (2015) Comparative analysis of genetic based approach and apriori algorithm for mining maximal frequent item sets. In: 2015 IEEE congress on evolutionary computation (CEC), pp 39–45. https://doi.org/10.1109/CEC.2015.7256872

  19. Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of ICNN’95—international conference on neural networks, vol 4, pp 1942–1948. https://doi.org/10.1109/ICNN.1995.488968

  20. Laurent A, Lesot MJ, Rifqi M (2009) Graank: exploiting rank correlations for extracting gradual itemsets. In: Proceedings of the 8th international conference on flexible query answering systems, FQAS ’09. Springer, Berlin, pp 382–393. https://doi.org/10.1007/978-3-642-04957-6_33

  21. Metzger A, Leitner P, Ivanović D, Schmieders E, Franklin R, Carro M, Dustdar S, Pohl K (2015) Comparing and combining predictive business process monitoring techniques. IEEE Trans Syst Man Cybern Syst 45(2):276–290

    Article  Google Scholar 

  22. Mirjalili S (2019) Genetic algorithm. Springer International Publishing, Cham, pp 43–55. https://doi.org/10.1007/978-3-319-93025-1_4

    Book  Google Scholar 

  23. Mishra S, Mishra D, Satapathy SK (2011) Particle swarm optimization based fuzzy frequent pattern mining from gene expression data. In: 2011 2nd international conference on computer and communication technology (ICCCT-2011), pp 15–20. https://doi.org/10.1109/ICCCT.2011.6075204

  24. Negrevergne B, Termier A, Rousset MC, Méhaut JF (2014) Paraminer: a generic pattern mining algorithm for multi-core architectures. Data Min Knowl Discov 28(3):593–633. https://doi.org/10.1007/s10618-013-0313-2

    Article  MathSciNet  MATH  Google Scholar 

  25. Owuor D, Laurent A, Orero J (2019) Mining fuzzy-temporal gradual patterns. In: 2019 IEEE international conference on fuzzy systems (FUZZ-IEEE). IEEE, New York, NY, USA, pp 1–6. https://doi.org/10.1109/FUZZ-IEEE.2019.8858883

  26. Patrício M, Pereira J, Crisóstomo J, Matafome P, Gomes M, Seiça R, Caramelo F (2018) Using resistin, glucose, age and BMI to predict the presence of breast cancer. BMC Cancer 18(1):29. https://doi.org/10.1186/s12885-017-3877-1

    Article  Google Scholar 

  27. Pourpanah F, Lim CP, Wang X, Tan CJ, Seera M, Shi Y (2019) A hybrid model of fuzzy min-max and brain storm optimization for feature selection and data classification. Neurocomputing 333:440–451. https://doi.org/10.1016/j.neucom.2019.01.011

    Article  Google Scholar 

  28. Rajamohana S, Umamaheswari K (2018) Hybrid approach of improved binary particle swarm optimization and shuffled frog leaping for feature selection. Comput Electr Eng 67:497–508. https://doi.org/10.1016/j.compeleceng.2018.02.015

    Article  Google Scholar 

  29. Runkler TA (2005) Ant colony optimization of clustering models. Int J Intell Syst 20(12):1233–1251. https://doi.org/10.1002/int.20111

    Article  MATH  Google Scholar 

  30. Saravanan M, Jyothi VL (2014) A novel approach for sequential pattern mining by using genetic algorithm. In: 2014 international conference on control, instrumentation, communication and computational technologies (ICCICCT), pp 284–288. https://doi.org/10.1109/ICCICCT.2014.6992971

  31. Shruti M, Debahuti M, Sandeep Ku S (2012) Fuzzy frequent pattern mining from gene expression data using dynamic multi-swarm particle swarm optimization. Procedia Technol 4:797–801. https://doi.org/10.1016/j.protcy.2012.05.130(2nd international conference on computer, communication, control and information technology (C3IT-2012))

  32. Silva CA, Runkler TA, Sousa JM, Palm R (2002) Ant colonies as logistic processes optimizers. In: Dorigo M, Di Caro G, Sampels M (eds) International workshop on ant algorithms. Springer, Berlin, pp 76–87

    Chapter  Google Scholar 

  33. Sim KM, Sun WH (2003) Multiple ant colony optimization for load balancing. In: Liu J, Cheung YM, Yin H (eds) Intelligent data engineering and automated learning. Springer, Berlin, pp 467–471

  34. Stützle T, Hoos HH (2000) Max-min ant system. Future Gener Comput Syst 16(8):889–914. https://doi.org/10.1016/S0167-739X(00)00043-1

    Article  MATH  Google Scholar 

  35. Uno T, Asai T, Uchida Y, Arimura H (2003) LCM: an efficient algorithm for enumerating frequent closed item sets. In: Proceedings of the IEEE ICDM workshop on frequent itemset mining implementations (FIMI ’03), vol 90

  36. Uno T, Asai T, Uchida Y, Arimura H (2004) LCM ver. 2: efficient mining algorithms for frequent/closed/maximal itemsets. In: Proceedings of the IEEE ICDM workshop on frequent itemset mining implementations (FIMI ’03), vol 126

  37. Vaz R, Shah V, Sawhney A, Deolekar R (2017) Automated big-o analysis of algorithms. In: 2017 international conference on nascent technologies in engineering (ICNTE), pp 1–6. https://doi.org/10.1109/ICNTE.2017.7947882

Download references

Acknowledgements

The authors would like to thank the French Government through the office of Co-operation and Cultural Service (Kenya) and the office of Campus France (Montpellier) for their involvement in creating the opportunity for this work to be produced. This work has been realized with the support of the High Performance Computing Platform: MESO@LR, financed by the Occitanie / Pyrénées-Méditerranée Region, Montpellier Mediterranean Metropole and Montpellier University.

Author information

Authors and Affiliations

  1. SCES Strathmore University, Nairobi, Kenya

    Dickson Odhiambo Owuor, Joseph Onderi Orero & Edmond Odhiambo Menya

  2. Siemens AG, Munich, Germany

    Thomas Runkler

  3. LIRMM Univ Montpellier, CNRS, Montpellier, France

    Anne Laurent

Authors
  1. Dickson Odhiambo Owuor
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Thomas Runkler
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Anne Laurent
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Joseph Onderi Orero
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Edmond Odhiambo Menya
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Dickson Odhiambo Owuor.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Owuor, D.O., Runkler, T., Laurent, A. et al. Ant colony optimization for mining gradual patterns. Int. J. Mach. Learn. & Cyber. 12, 2989–3009 (2021). https://doi.org/10.1007/s13042-021-01390-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13042-021-01390-w

Keywords

Search

Navigation