Advances in Knowledge Discovery and Data Mining: 14th by Bin Tong, Einoshin Suzuki (auth.), Mohammed J. Zaki, Jeffrey PDF

By Bin Tong, Einoshin Suzuki (auth.), Mohammed J. Zaki, Jeffrey Xu Yu, B. Ravindran, Vikram Pudi (eds.)

ISBN-10: 3642136710

ISBN-13: 9783642136719

ISBN-10: 3642136729

ISBN-13: 9783642136726

This publication constitutes the lawsuits of the 14th Pacific-Asia convention, PAKDD 2010, held in Hyderabad, India, in June 2010.

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Read Online or Download Advances in Knowledge Discovery and Data Mining: 14th Pacific-Asia Conference, PAKDD 2010, Hyderabad, India, June 21-24, 2010. Proceedings. Part II PDF

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Extra info for Advances in Knowledge Discovery and Data Mining: 14th Pacific-Asia Conference, PAKDD 2010, Hyderabad, India, June 21-24, 2010. Proceedings. Part II

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Network cost reported as a fraction of the worst case bound RequiredM essages W orstCaseBound D-Isomap deployed with LMDS. We used MATLAB R2008a for the implementation of the algorithms and E2LSH [2] for LSH. Due to space limitations, we report only a subset of the experiments1 . 3. First we validated the bound of Theorem 1 with the Swiss Roll. The results (Figures 2(a), 2(b)) indicate a reduction in the number of messages; consequently we employed the bounded version of the algorithm for all experiments.

9) ξ is expected to be minimized in order to preserve the sub-manifold of data. At last, the final objective function that combines Eq. 7 and Eq. W T W =I where λ is a parameter to control the impact of manifold regularization. By introducing the Lagrangian, the objective function is given by the maximum eigenvalue solution to the following generalized eigenvector problem: X(P − Q − λM)X T w = φw (11) where φ is the eigenvalue of P − Q − λM , and w is the corresponding eigenvector. One may argue that, when the graph of SNN is equal to the k-NN graph of the manifold regularization, Q is almost equivalent to M on preserving the local structure.

Fedra: A fast and efficient dimensionality reduction algorithm. In: SIAM SDM, pp. 509–520 (2009) 26 P. Magdalinos, M. Vazirgiannis, and D. Valsamou 13. : Global pca for dimensionality reduction in distributed data mining. In: SDMKD, ch. 19, pp. 327–342. CRC, Boca Raton (2004) 14. : Pca for dimensionality reduction in massive distributed data sets. In: 5th International Workshop on High Performance Data Mining (2002) 15. : A scalable contentaddressable network. In: ACM SIGCOMM, pp. 161–172 (2001) 16.

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Advances in Knowledge Discovery and Data Mining: 14th Pacific-Asia Conference, PAKDD 2010, Hyderabad, India, June 21-24, 2010. Proceedings. Part II by Bin Tong, Einoshin Suzuki (auth.), Mohammed J. Zaki, Jeffrey Xu Yu, B. Ravindran, Vikram Pudi (eds.)


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