Abstract
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.
The research leading to the results described in the paper has received funding from the European Community’s Seventh Framework Program (FP7/2007-2013) under grant agreement n° 218086.
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References
Seifert, J.W.: Data mining: An overview. CRS Report for Congress (2004)
i2 Ltd: i2 Pattern Tracer (2009) http://www.i2.co.uk/
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)
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)
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)
Bodon, F.: A fast apriori implementation. In: Proceedings of the IEEE ICDM Workshop on Frequent Itemset Mining Implementations. CEUR Workshop Proceedings, vol. 90 (2003)
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)
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)
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)
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)
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)
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)
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)
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)
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Bęben, Ł., Śnieżyński, B. (2013). Multiagent System for Pattern Searching in Billing Data. In: Dziech, A., Czyżewski, A. (eds) Multimedia Communications, Services and Security. MCSS 2013. Communications in Computer and Information Science, vol 368. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38559-9_2
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DOI: https://doi.org/10.1007/978-3-642-38559-9_2
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