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Extra resources for Carrier System and Applications
Data Mining and Knowledge Discovery, 1(3), 259–289. , & Teisseire, M. (2003). Incremental mining of sequential patterns in large databases. Data & Knowledge Engineering, 46(1), 97–121. , & Nakagawa, M. (2002). Using sequential and non-sequential patterns in predictive Web usage mining tasks. ICDM ‘02: Proceedings of the 2002 IEEE International Conference on Data Mining (pp. 669-672). Washington, DC: IEEE Computer Society. Nicolas, J. , & Albuisson, E. (2004). Sequential pattern mining and classification of patient path.
In this context, event correlation is of prime importance to extract meaningful information from the wealth of alarm data generated by the network. Existing sequential data mining techniques address the task of identifying possible correlations in sequences of alarms. The output sequence sets, however, may contain sequences which are not plausible from the point of view of network topology constraints. (Devitt, Duffin, & Moloney, 2005) presents the Topographical Proximity (TP) approach which exploits topographical information embedded in alarm data in order to address this lack of plausibility in mined sequences.
Their approach is based on an Apriori approach and introduces a novel criterion for sequence selection which evaluates sequence plausibility and coherence in the context of network topology. Connections are inferred at run-time between pairs of alarm generating nodes in the data and a Topographical Proximity (TP) measure is assigned based on the strength of the inferred connection. e. the strength of their connection, thereby reducing the candidate sequence set and optimizing the space and time constraints of the data mining process.
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