Multiagent System for Pattern Searching in Billing Data

  • Łukasz Bęben
  • Bartłomiej Śnieżyński
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 368)


In this paper we present an agent-based pattern searching system using a distributed Apriori algorithm to analyse billing data. In the paper, we briefly present the problem of pattern mining. Next, we discuss related research focusing on distributed versions of Apriori algorithm and agent-based data mining software. Paper continues with an explanation of architecture and algorithms used in the system. We propose an original distribution mechanism allowing to split data into smaller chunks and also orthogonally distribute candidate patterns support calculation (in the same computation task). Experimental results on both generated and real-world data show that for different conditions other distribution policies give better speedup. The system is implemented using Erlang and can be used in heterogeneous hardware environment. This, together with multi-agent architecture gives flexibility in the system configuration and extension.


data mining multi-agent systems criminal analysis 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Seifert, J.W.: Data mining: An overview. CRS Report for Congress (2004)Google Scholar
  2. 2.
    i2 Ltd: i2 Pattern Tracer (2009)
  3. 3.
    Świerczek, A., Dębski, R., Włodek, P., Śnieżyński, B.: Integrating applications developed for heterogeneous platforms: Building an environment for criminal analysts. In: Dziech, A., Czyżewski, A. (eds.) MCSS 2011. CCIS, vol. 149, pp. 19–27. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  4. 4.
    Yu, K.M., Zhou, J.L.: A weighted load-balancing parallel apriori algorithm for association rule mining. In: IEEE International Conference on Granular Computing, GrC 2008, pp. 756–761 (August 2008)Google Scholar
  5. 5.
    Ye, Y., Chiang, C.C.: A parallel apriori algorithm for frequent itemsets mining. In: Fourth International Conference on Software Engineering Research, Management and Applications, pp. 87–93 (August 2006)Google Scholar
  6. 6.
    Bodon, F.: A fast apriori implementation. In: Proceedings of the IEEE ICDM Workshop on Frequent Itemset Mining Implementations. CEUR Workshop Proceedings, vol. 90 (2003)Google Scholar
  7. 7.
    Lin, M.Y., Lee, P.Y., Hsueh, S.C.: Apriori-based frequent itemset mining algorithms on mapreduce. In: ICUIMC 2012 Proceedings of the 6th International Conference on Ubiquitous Information Management and Communication Article No. 76 (2012)Google Scholar
  8. 8.
    Moemeng, C., Gorodetsky, V., Zuo, Z., Yang, Y., Zhang, C.: Agent-based distributed data mining: A survey. In: Cao, L. (ed.) Data Mining and Multi-agent Integration, pp. 47–58. Springer, US (2009)CrossRefGoogle Scholar
  9. 9.
    Klusch, M., Lodi, S., Gianluca, M.: The role of agents in distributed data mining: issues and benefits. In: IEEE/WIC International Conference on Intelligent Agent Technology, IAT 2003, pp. 211–217 (October 2003)Google Scholar
  10. 10.
    Li, X., Ni, J.: Deploying mobile agents in distributed data mining. In: Washio, T., Zhou, Z.-H., Huang, J.Z., Hu, X., Li, J., Xie, C., He, J., Zou, D., Li, K.-C., Freire, M.M. (eds.) PAKDD 2007. LNCS (LNAI), vol. 4819, pp. 322–331. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  11. 11.
    Kargupta, H., Hamzaoglu, I., Stafford, B.: Scalable, distributed data mining using an agent based architecture. In: Proceedings the Third International Conference on the Knowledge Discovery and Data Mining, pp. 211–214. AAAI Press, Menlo Park (1997)Google Scholar
  12. 12.
    Śnieżyński, B.: An architecture for learning agents. In: Bubak, M., van Albada, G.D., Dongarra, J., Sloot, P.M.A. (eds.) ICCS 2008, Part III. LNCS, vol. 5103, pp. 722–730. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  13. 13.
    Wilaszek, K., Wójcik, T., Opaliński, A., Turek, W.: Internet identity analysis and similarities detection. In: Dziech, A., Czyżewski, A. (eds.) MCSS 2012. CCIS, vol. 287, pp. 369–379. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  14. 14.
    Turek, W., Opalinski, A., Kisiel-Dorohinicki, M.: Extensible web crawler – towards multimedia material analysis. In: Dziech, A., Czyżewski, A. (eds.) MCSS 2011. CCIS, vol. 149, pp. 183–190. Springer, Heidelberg (2011)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Łukasz Bęben
    • 1
  • Bartłomiej Śnieżyński
    • 1
  1. 1.Faculty of Computer Science, Electronics and Telecommunications, Department of Computer ScienceAGH University of Science and TechnologyKrakowPoland

Personalised recommendations