Abstract
More and more data come with contextual information describing the circumstances of their acquisition. While the frequent pattern mining literature offers a lot of approaches to handle and extract interesting patterns in data, little effort has been dedicated to relevantly handling such contextual information during the mining process. In this paper we propose a generic formulation of the contextual frequent pattern mining problem and provide the CFPM algorithm to mine frequent patterns that are representative of a context. This approach is generic w.r.t. the pattern language (e.g., itemsets, sequential patterns, subgraphs, etc.) and therefore is applicable in a wide variety of use cases. The CFPM method is experimented on real datasets with three different pattern languages to assess its performances and genericity.
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References
Agrawal, R., Imieliński, T., Swami, A.: Mining association rules between sets of items in large databases. In: ACM SIGMOD Record, vol. 22, pp. 207–216. ACM (1993)
Agrawal, R., Srikant, R.: Mining sequential patterns. In: International Conference on Data Engineering, pp. 3–14. IEEE (1995)
Bringmann, B., Nijssen, S.: What is frequent in a single graph? In: Washio, T., Suzuki, E., Ting, K.M., Inokuchi, A. (eds.) PAKDD 2008. LNCS (LNAI), vol. 5012, pp. 858–863. Springer, Heidelberg (2008)
Bringmann, B., Nijssen, S., Zimmermann, A.: Pattern-based classification: A unifying perspective. In: LeGo Workshop, p. 36 (2009)
Fournier-Viger, P., Gomariz, A., Soltani, A., Lam, H., Gueniche, T.: Spmf: Open-source data mining platform (2014), http://www.philippe-fournier-viger.com/spmf
Han, J., Cheng, H., Xin, D., Yan, X.: Frequent pattern mining: current status and future directions. Data Mining and Knowledge Discovery 15(1), 55–86 (2007)
Hansen, K., Mika, S., Schroeter, T., Sutter, A., ter Laak, A., Steger-Hartmann, T., Heinrich, N., Müller, K.-R.: Benchmark data set for in silico prediction of ames mutagenicity. Journal of Chemical Information and Modeling (2009)
Jindal, N., Liu, B.: Opinion spam and analysis. In: International Conference on Web Search and Data Mining, pp. 219–230. ACM (2008)
Kuramochi, M., Karypis, G.: Frequent subgraph discovery. In: International Conference on Data Mining, pp. 313–320. IEEE (2001)
Mannila, H., Toivonen, H.: Levelwise search and borders of theories in knowledge discovery. Data Mining and Knowledge Discovery 1(3), 241–258 (1997)
Pei, J., Pinto, H., Chen, Q., Han, J., Mortazavi-Asl, B., Dayal, U., Hsu, M.-C.: Prefixspan: Mining sequential patterns efficiently by prefix-projected pattern growth. In: 2013 IEEE 29th International Conference on Data Engineering (ICDE), p. 0215. IEEE Computer Society (2001)
Rabatel, J., Bringay, S., Poncelet, P.: Contextual sequential pattern mining. In: International Conference on Data Mining Workshops, pp. 981–988. IEEE (2010)
Rabatel, J., Bringay, S., Poncelet, P.: Mining sequential patterns: A context-aware approach. In: Guillet, F., Pinaud, B., Venturini, G., Zighed, D.A. (eds.) Advances in Knowledge Discovery and Management. SCI, vol. 471, pp. 23–41. Springer, Heidelberg (2013)
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Rabatel, J., Bringay, S., Poncelet, P. (2014). Mining Representative Frequent Patterns in a Hierarchy of Contexts. In: Blockeel, H., van Leeuwen, M., Vinciotti, V. (eds) Advances in Intelligent Data Analysis XIII. IDA 2014. Lecture Notes in Computer Science, vol 8819. Springer, Cham. https://doi.org/10.1007/978-3-319-12571-8_21
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DOI: https://doi.org/10.1007/978-3-319-12571-8_21
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