Abstract
The collective classification problem for big data sets using MapReduce programming model was considered in the paper. We introduced a proposal for implementation of label propagation algorithm in the network. The method was examined on real dataset in telecommunication domain. The results indicated that it can be used to classify nodes in order to propose new offerings or tariffs to customers.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Ekanayake, J., Pallickara, S., Fox, G.: MapReduce for Data Intensive Scientific Analyses. In: Proceedings of the 2008 Fourth IEEE International Conference on eScience (2008)
Dean, J., Ghemawat, S.: Mapreduce: simplified data processing on large clusters. In: Proceedings of the 6th Conference on Symposium on Opearting Systems Design & Implementation, pp. 10–24. USENIX Association, Berkeley (2004)
White, T.: Hadoop: The Definitive Guide. O’Reilly (2009)
Hadoop official web site (November 05, 2011), hadoop.apache.org
Szummer, M., Jaakkola, T.: Clustering and efficient use of unlabeled examples. In: Proceedings of Neural Information Processing Systems, NIPS (2001)
Zhu, X., Ghahramani, Z., Lafferty, J.: Semi-supervised learning using Gaussian fields and harmonic functions. In: Proceedings of the International Conference on Machine Learning, ICML (2003)
Azran, A.: The rendezvous algorithm: Multiclass semi-supervised learning with markov random walks. In: Proceedings of the International Conference on Machine Learning, ICML (2007)
Jensen, D., Neville, J., Gallagher, B.: Why collective inference improves relational classification. In: The Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 593–598 (2004)
Desrosiers, C., Karypis, G.: Within-Network Classification Using Local Structure Similarity. In: Buntine, W., Grobelnik, M., Mladenić, D., Shawe-Taylor, J. (eds.) ECML PKDD 2009. LNCS, vol. 5781, pp. 260–275. Springer, Heidelberg (2009)
Knobbe, A., de Haas, M., Siebes, A.: Propositionalisation and Aggregates. In: Siebes, A., De Raedt, L. (eds.) PKDD 2001. LNCS (LNAI), vol. 2168, pp. 277–288. Springer, Heidelberg (2001)
Kramer, S., Lavrac, N., Flach, P.: Propositionalization approaches to relational data mining. In: Dezeroski, S. (ed.) Relational Data Mining, pp. 262–286. Springer (2001)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Indyk, W., Kajdanowicz, T., Kazienko, P., Plamowski, S. (2012). MapReduce Approach to Collective Classification for Networks. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2012. Lecture Notes in Computer Science(), vol 7267. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29347-4_76
Download citation
DOI: https://doi.org/10.1007/978-3-642-29347-4_76
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-29346-7
Online ISBN: 978-3-642-29347-4
eBook Packages: Computer ScienceComputer Science (R0)