By A. I. Markushevich
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Additional resources for Curvas Maravillosas, Numeros Complejos y Representaciones Conformes, Funciones Maravillosas
Asano, and M. Yoshikawa Acknowledgment This work was supported in part by the National Institute of Information and Communications Technology, Japan. References 1. : Analysis of implicit relations on Wikipedia: Measuring strength through mining elucidatory objects. , Watanabe, C. ) DASFAA 2010. LNCS, vol. 5981, pp. 460–475. Springer, Heidelberg (2010) 2. : Measuring and extracting proximity in networks. In: Proc. of 12th ACM SIGKDD Conference, pp. 245–255 (2006) 3. : Fast discovery of connection subgraphs.
Improving web search results using affinity graph. In: Proc. of 28th SIGIR, pp. 504–511 (2005) 5. : Less is more: probabilistic models for retrieving fewer relevant documents. In: Proc. of 29th SIGIR, pp. 429–436 (2006) 6. : Novelty and diversity in information retrieval evaluation. In: Proc. of 31th SIGIR, pp. 659–666 (2008) 7. : Center-piece subgraphs: Problem definition and fast solutions. In: Proc. of 12th ACM SIGKDD Conference, pp. 404–413 (2006) 8. : Context-aware object connection discovery in large graphs.
We thus propose two more advanced solutions that take advantages of both naive solutions. Our advanced solutions can be seen as an engineering approach that preserves uncertainty and distance orders as much as possible through the recent wavelet-based perturbation and the noise averaging eﬀect of PAA. Our solutions provide a very practical approach to publish time-series data which well preserves mining accuracy as well as privacy. We do not use any complicated cryptography techniques or SMC protocols , but simply adopt an intuitive notion of the noise averaging eﬀect and the wavelet-based secure perturbation method.
Curvas Maravillosas, Numeros Complejos y Representaciones Conformes, Funciones Maravillosas by A. I. Markushevich