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Research on the Application of Ant Colony Clustering in Commodity Classification

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Artificial Intelligence and Security (ICAIS 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13339))

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Abstract

As a swarm intelligence algorithm, ant colony algorithm is simple to implement, and its application range and fields are very wide, it can also effectively solve complex problems, especially unsupervised clustering problems and combinatorial optimization problems. At the same time, ant colony algorithm has the characteristics of self-organization, scalability, robustness and anti-noise data. Since the ant colony algorithm is a global search, it can be applied to the e-commerce system to deal with the problems of cold start and data scarcity, the ant colony clustering model is closer to the actual clustering problem. In this paper, ant colony algorithm is used to cluster users according to user behavior characteristics to achieve the purpose of product recommendation. By constructing the pheromone matrix and the optimal solution set of the ant colony, the iterative results of the algorithm are stored. On this basis, iterate continuously, mark the behavior data of different users, and update the optimal solution to find the category of each user. Secondly, through different parameter settings, the experimental results are analyzed. If the clustering algorithm can be effectively applied in the e-commerce system, the system can improve the accuracy and efficiency of the recommendation results when making personalized recommendations for users.

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References

  1. Liu, F., Li, Y.: Improved population classification ant colony algorithm and its application. Comput. Syst. Appl. 19(01), 144–148 (2010)

    Google Scholar 

  2. Guo, Q., Pan, X.: Research on personalized recommendations in e-commerce. E-commerce 2020, 50–51 (2020). https://doi.org/10.14011/j.cnki.dzsw

  3. Jiang, X., Gao, S.: Image segmentation based on K-means and ant colony hybrid clustering. Comput. Digital Eng. 39(06), 138–141 (2011)

    Google Scholar 

  4. Ye, J., Fu, Q., He, Y., Ye, H.: An ant colony algorithm based on clustering ensemble to solve large-scale TSP problems. Comput. Modernization 02, 31–35 (2020)

    Google Scholar 

  5. Jia, R., Wang, H.: Cluster analysis based on improved ant colony algorithm. Comput. Appl. Softw. 27(12), 97–100 (2010)

    Google Scholar 

  6. Li, W.: Research on ant colony clustering algorithm based on partition. Inf. Comput. 02, 045–047 (2019)

    Google Scholar 

  7. Ge, Z., Yan, G., Zhang, G.: A bipartite graph network recommendation algorithm based on ant colony clustering. Inf. Technol. 2016, 57–61 (2016). https://doi.org/10.13274/j.cnki.hdzj.2016.03.014

  8. Li, Z., Jia, R.: An improved k-means ant colony clustering algorithm. Comput. Technol. Dev. 12, 28–31 (2015)

    Google Scholar 

  9. Wang, X., Luo, K.: LF ant colony clustering algorithm with global memory. Comput. Eng. Appl. 55(20), 52–57 (2019)

    Google Scholar 

  10. Tao, T., Wang, J., Liu, Z., Chen, X., Feng, W.: Cluster analysis of panel custom furniture orders based on ant colony clustering algorithm. Forest Prod. Ind. 05, 0049–0052 (2020)

    Google Scholar 

  11. Chen, X., Wan, L., Li, H., Li, C.: Roller failure prediction based on ant colony optimization K-means clustering algorithm. Comput. Eng. Des. 11, 3218–3223 (2020)

    Google Scholar 

  12. Zhou, X., Hong, C.: The application of ant colony clustering algorithm in customer classification. Comput. Modernization 05, 0033–0035 (2007)

    Google Scholar 

  13. Gao, W.: Application research of ant colony algorithm in collaborative filtering recommendation system. Microcomput. Inf. 03, 0268–0270 (2008)

    Google Scholar 

  14. Li, L., Wei, Y.: Collaborative filtering microblog recommendation algorithm based on fusion of tags and ant colony. Softw. Guide 07, 0083–0086 (2018)

    Google Scholar 

  15. Zhang, J., Jiang, H., Zhang, X.: A review of ant colony clustering algorithms. Comput. Eng. Appl. 16, 0171–0174 (2006)

    Google Scholar 

  16. Wu, Q., Zhang, Y., Ma, Z.: Overview of ant colony algorithm. Microcomput. Inf. 03, 01–02 (2011)

    Google Scholar 

  17. Colorni, A., Dorigo, M., Maniezzo, V.: Distributed optimization by ant colonies. In: Proceedings of European Conference on Artificial Life, pp. 134–142 (1991)

    Google Scholar 

  18. Colorni, A., Dorigo, M., Maniezzo, V.: An investigation of some properties of an ant algorithm. In: Proceedings of Parallel Problem Solving from Nature (PPSN). Elsevier, France (1992)

    Google Scholar 

  19. Dorigo, M.: Optimization learning and natural algorithms. Politecnico di, Milano, Italy (1992)

    Google Scholar 

  20. Dorigo, M., Maniezzo, V., Colorni, A.: Ant system: optimization by a colony of coorperating agents. IEEE Trans. SMC 26(01), 29–41 (1996)

    Google Scholar 

  21. Dorigo, M., Gambardella, L.: Ant colony system: a cooperative learning approach to the traveling salesman problem. IEEE Trans. Evol. Comput. 01, 53–66 (1997)

    Article  Google Scholar 

  22. Dorigo, M., Caro, G.D., Gambardella, L.M.: Ant algorithms for discrete optimization. In: Proceedings of the Congress on Evolutionary Computation, pp. 137–172 (1999)

    Google Scholar 

  23. Kanungo, T., Mount, D.M., Netanyahu, N.S.: An efficient K-means clustering algorithm analysis and implementation. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 881–892 (2002)

    Article  Google Scholar 

  24. Inkaya, T., Kayalıgil, S., Özdemirel, N.E.: Ant colony optimization based clustering method-ology. Appl. Soft Comput. 28, 301–311 (2015)

    Article  Google Scholar 

  25. Zhou, P., Shi, W., Tian, J., et al.: Attention-based bidirectional long short-term memory networks for relation classification. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, Berlin, Germany, pp. 207–212 (2016)

    Google Scholar 

  26. Chavarria-Molina, J., Fallas-Monge, J.J., Trejos-Zelaya, J.: Clustering via ant colonies: parameter analysis and improvement of the algorithm (2019)

    Google Scholar 

  27. Bonabeau, E., Dorigo, M., Theraulaz, G.: Swarm Intelligence-From Natural to Artificial System. Oxford University Press, Oxford (1999)

    Google Scholar 

  28. Deneubourg, J.L., Goss, S., Franks, N.: The dynamics of collective sorting: Robot- like ants and ant-like robots. In: Meyer, C.J.A., Wilson, S.W. (eds.) Proceedings of the First International Conference on Simulation of Adaptive Behaviour, From Animals to Animals, pp. 356–365. MIT Press (1991)

    Google Scholar 

  29. Calabrò, G., Torrisi, V., Inturri, G., Ignaccolo, M.: Improving inbound logistic planning for large-scale real-world routing problems: a novel ant-colony simulation based optimization. Eur. Transp. Res. Rev. 12, 28–28 (2020). https://doi.org/10.1186/s12544-020-00409-7

    Article  Google Scholar 

  30. Calabrò, G., Inturri, G., Pira, M.L., Pluchino, A., Ignaccolo, M.: Bridging the gap between weak-demand areas and public transport using an ant-colony simulation-based optimization. Transp. Res. Procedia 45, 234–241 (2020)

    Article  Google Scholar 

  31. Ibrahim, E.S., Birchell, S., Elfayoumy, S.: Automatic heart volume measurement from CMR images using ant colony optimization with iterative salient isolated thresholding. J Cardiovasc. Magn. Reason. 14, 286–286 (2012)

    Article  Google Scholar 

  32. Hessler, G., Korb, O., Monecke, P., Stützle, T., Exner, T.E.: pharmACOphore: multiple flexible ligand alignment based on ant colony optimization. J Cheminformatics 2, 17–17 (2010)

    Article  Google Scholar 

  33. Moffett, M.W., Garnier, S., Eisenhardt, K.M.: Correction to: ant colonies: building com- plex organizations with minuscule brains and no leaders. J. Organ. Des. (2021)

    Google Scholar 

  34. Grze, I.M., et al.: Colony size and brood investment of Myrmica rubra ant colonies in habitats invaded by goldenrods. Insectes Sociaux 65(2), 275–280 (2018). https://doi.org/10.1007/s00040-018-0612-0

    Article  Google Scholar 

  35. Wang, X., Wang, Q.: Application of dynamic programming algorithm based on model predictive control in hybrid electric vehicle control strategy. J. Internet Things 02(02), 81–87 (2020)

    Article  Google Scholar 

  36. Alsaedi, N.H., Jaha, E.S.: Dynamic audio-visual biometric fusion for person recognition. Comput. Mater. Continua 71(01), 1283–1311 (2022)

    Article  Google Scholar 

  37. Rajeswari, P., Jayashree, K.: Hybrid metaheuristics web service composition model for Qos aware services. Comput. Syst. Sci. Eng. 41(02), 511–524 (2022)

    Article  Google Scholar 

  38. Manjula, P., Priya, D.S.B.: Intelligent chimp metaheuristics optimization with data encryption protocol for WSN. Intell. Autom. Soft Comput. 32(01), 573–587 (2022)

    Google Scholar 

  39. Mahmood, S.: Review of internet of things in different sectors: recent advance, technologies, and challenges. J. Internet Things 01, 19–26 (2021)

    Article  Google Scholar 

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Acknowledgement

I would like to express my gratitude to all those who helped me during the writing of this thesis. My deepest gratitude goes first and foremost to Dr. Sun, my supervisor, for his constant encouragement and guidance. He has walked me through all the stages of the writing of this thesis. Without his consistent and illuminating instruction, this thesis could not have reached its present form. Second, I also owe my sincere gratitude to my friends and my fellow classmates who gave me their help and time in listening to me and helping me work out my problems during the difficult course of the thesis. Last my thanks would go to my beloved family for their loving considerations and great confidence in me all through this thesis.

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Authors and Affiliations

  1. Hunan University of Finance and Economics, Changsha, 410000, China

    Zuyan Li & Guang Sun

Authors
  1. Zuyan Li
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  2. Guang Sun
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Corresponding author

Correspondence to Guang Sun .

Editor information

Editors and Affiliations

  1. Nanjing University of Information Science and Technology, Nanjing, China

    Xingming Sun

  2. Nanjing University of Information Science and Technology, Nanjing, China

    Xiaorui Zhang

  3. Jinan University, Guangzhou, China

    Zhihua Xia

  4. Purdue University, West Lafayette, IN, USA

    Elisa Bertino

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Li, Z., Sun, G. (2022). Research on the Application of Ant Colony Clustering in Commodity Classification. In: Sun, X., Zhang, X., Xia, Z., Bertino, E. (eds) Artificial Intelligence and Security. ICAIS 2022. Lecture Notes in Computer Science, vol 13339. Springer, Cham. https://doi.org/10.1007/978-3-031-06788-4_28

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  • DOI: https://doi.org/10.1007/978-3-031-06788-4_28

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-06787-7

  • Online ISBN: 978-3-031-06788-4

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