A Multi-Agent Architecture for Distributed Data Mining Systems

  • Gustavo H. B. S. Oliveira IFMA
  • Josenildo C. da Silva IFMA
  • Omar A. C. Cortes IFMA
  • Luciano R. Coutinho UFMA

Resumo


The agent-based approach is appealing to Distributed Data Mining (DDM) systems since the concept of agency offers some relevant features, such as scalability, flexibility, robustness, and modularity. This paper investigates whether and how the multi-agent system metaphor might be used for Distributed Data Mining Systems. We proposed and implemented a multi-agent architecture called SeAMS, which is capable of mining patterns efficiently using the DPDTS algorithm. The system was developed in JADE and designed to be easily extensible and protect any local datasets' privacy. Results show that the agentbased approach was able to identify patterns efficiently using three different popular scientific datasets in a distributed time series: Sunspot, Power, and TEK.

Palavras-chave: Multi-Agent, Distributed, Data Mining, Jade

Referências

Albashiri, K. A., Coenen, F., and Leng, P. (2009). Emads: An extendible multi-agent data miner. Knowledge-Based Systems, 22(7):523-528.

Bellifemine, F. L., Caire, G., and Greenwood, D. (2007). Developing multi-agent systems with JADE. John Wiley & Sons.

Chaimontree, S., Atkinson, K., and Coenen, F. (2010). Multi-agent based clustering: Towards generic multi-agent data mining. In Industrial Conference on Data Mining, pages 115-127. Springer.

da Silva, J. C., Cortes, O. A. C., Oliveira, G. H. B., and Klusch, M. (2012). Density-based pattern discovery in distributed time series. In Proc. of the 21st Brazilian Symp. on Artificial Intelligence (SBIA), pages 62-71. Springer.

da Silva, J. C., Giannella, C., Bhargava, R., Kargupta, H., and Klusch, M. (2005). Distributed data mining and agents. Engineering Applications of Artifical Intelligence, 4(18):791-807.

Di Fatta, G. and Fortino, G. (2007). A customizable multi-agent system for distributed data mining. In Proceedings of the 2007 ACM Symposium on Applied Computing, SAC '07, pages 42-47, New York, NY, USA. ACM.

Golzadeh, M., Hadavandi, E., and Chehreh Chelgani, S. (2018). A new ensemble based multi-agent system for prediction problems: Case study of modeling coal free swelling index. Applied Soft Computing, 64:109-125.

Hafezi, R., Shahrabi, J., and Hadavandi, E. (2015). A bat-neural network multi-agent system (bnnmas) for stock price prediction. Appl. Soft Comput., 29(C):196-210.

Keogh, E., Zhu, Q., Hu, B., Y., H., Xi, X., Wei, L., and Ratanamahatana, C. A. (2011). The ucr time series classification/clustering homepage. Availabe at https://www.cs.ucr.edu/~eamonn/time_series_data/.

Zeng, L., Li, L., Duan, L., Lu, K., Shi, Z., Wang, M., Wu, W., and Luo, P. (2012). Distributed data mining: a survey. Information Technology and Management, 13(4):403-409.
Publicado
31/07/2022
OLIVEIRA, Gustavo H. B. S.; SILVA, Josenildo C. da; CORTES, Omar A. C.; COUTINHO, Luciano R.. A Multi-Agent Architecture for Distributed Data Mining Systems. In: BRAZILIAN E-SCIENCE WORKSHOP (BRESCI), 16. , 2022, Niterói. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2022 . p. 1-8. ISSN 2763-8774. DOI: https://doi.org/10.5753/bresci.2022.222487.