Abstract
Context-aware applications allow service providers to adapt their services to actual user needs, by offering them personalized services depending on their current application context. Hence, service providers are usually interested in profiling users both to increase client satisfaction, and to broaden the set of offered services.
Since association rule extraction allows the identification of hidden correlations among data, its application in context-aware platforms is very attractive. However, traditional association rule extraction, driven by support and confidence constraints, may entail either (i) generating an unmanageable number of rules in case of low support thresholds, or (ii) discarding rare (infrequent) rules, even if their hidden knowledge might be relevant to the service provider. Novel approaches are needed to effectively manage different data granularities during the mining activity.
This paper presents the CAS-Mine framework to efficiently discover relevant relationships between user context data and currently asked services for both user and service profiling. CAS-Mine exploits a novel and efficient algorithm to extract generalized association rules. Support driven opportunistic aggregation is exploited to exclusively generalize infrequent rules. User-provided taxonomies on different attributes (e.g., a geographic hierarchy on spatial coordinates, a temporal hierarchy, a classification of provided services), drive the rule generalization process that prevents discarding relevant but infrequent knowledge.
Experiments performed on both real and synthetic datasets show the effectiveness and the efficiency of the proposed framework in mining different types of correlations between user habits and provided services.
This work was supported by a grant from Telecom Italia Lab.
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References
Agrawal, R., Srikant, R.: Fast Algorithms for Mining Association Rules in Large Databases. In: Proceedings of the 20th VLDB conference, pp. 487–499 (1994)
Baralis, E., Cerquitelli, T., D’Elia, V.: Generalized itemset discovery by means of opportunistic aggregation. Technical report, Politecnico di Torino (2009), https://dbdmg.polito.it/twiki/bin/view/Public/NetworkTrafficAnalysis
Goethals, B.: Frequent Pattern Mining Implementations, http://www.adrem.ua.ac.be/~goethals/software
Bradley, N.A., Dunlop, M.D.: Toward a multidisciplinary model of context to support context-aware computing. Hum.-Comput. Interact. 20(4), 403–446 (2005)
Byun, H., Cheverst, K.: Utilizing context history to provide dynamic adaptations. Applied Artificial Intelligence 18(6), 533–548 (2004)
Hakkila, J., Mantyjarvi, J.: Collaboration in context-aware mobile phone applications. In: Hawaii International Conference on System Sciences, vol. 1, p. 33 (2005)
Han, J., Fu, Y.: Mining multiple-level association rules in large databases. IEEE Trans. Knowl. Data Eng. 11(5), 798–804 (1999)
Hong, J., Suh, E., Kim, S.: Context-aware systems: A literature review and classification. In: Expert Systems with Applications (November 2008)
Khedo, K.K.: Context-aware systems for mobile and ubiquitous networks. In: ICNICONSMCL 2006, p. 123 (2006)
Nurmi, P., Salden, A., Lau, S.L., Suomela, J., Sutterer, M., Millerat, J., Martin, M., Lagerspetz, E., Poortinga, R.: A system for context-dependent user modeling. In: Meersman, R., Tari, Z., Herrero, P. (eds.) OTM 2006 Workshops. LNCS, vol. 4278, pp. 1894–1903. Springer, Heidelberg (2006)
Oliver, N., Garg, A., Horvitz, E.: Layered representations for learning and inferring office activity from multiple sensory channels. Comput. Vis. Image Underst. 96(2), 163–180 (2004)
Tapia, E.M., Intille, S.S., Larson, K.: Activity recognition in the home using simple and ubiquitous sensors. In: Ferscha, A., Mattern, F. (eds.) PERVASIVE 2004. LNCS, vol. 3001, pp. 158–175. Springer, Heidelberg (2004)
TPC-H. The TPC benchmark H. Transaction Processing Performance Council (2009), http://www.tpc.org/tpch/default.asp
Uno, T., Kiyomi, M., Arimura, H.: LCM ver. 2: Efficient mining algorithms for frequent/closed/maximal itemsets. In: FIMI (2004)
Vajirkar, P., Singh, S., Lee, Y.: Context-aware data mining framework for wireless medical application. In: Mařík, V., Štěpánková, O., Retschitzegger, W. (eds.) DEXA 2003. LNCS, vol. 2736, pp. 381–391. Springer, Heidelberg (2003)
Zukerman, I., Albrecht, D.W.: Predictive statistical models for user modeling. User Modeling and User-Adapted Interaction 11(1-2), 5–18 (2001)
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Baralis, E., Cagliero, L., Cerquitelli, T., Garza, P., Marchetti, M. (2009). Context-Aware User and Service Profiling by Means of Generalized Association Rules. In: Velásquez, J.D., Ríos, S.A., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based and Intelligent Information and Engineering Systems. KES 2009. Lecture Notes in Computer Science(), vol 5712. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04592-9_7
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DOI: https://doi.org/10.1007/978-3-642-04592-9_7
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