Download PDF by Mohammed J. Zaki, Jeffrey Xu Yu, B. Ravindran, Vikram Pudi: Advances in Knowledge Discovery and Data Mining, Part II:

By Mohammed J. Zaki, Jeffrey Xu Yu, B. Ravindran, Vikram Pudi

ISBN-10: 3642136710

ISBN-13: 9783642136719

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

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

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A prominent such application is knowledge discovery from text collections distributed in a P2P network. Latest theoretical and experimental evidence point out that documents lay on a non linear high dimensional manifold ([5],[3]). J. Zaki et al. ): PAKDD 2010, Part II, LNAI 6119, pp. 14–26, 2010. c Springer-Verlag Berlin Heidelberg 2010 Distributed Knowledge Discovery with Non Linear Dimensionality Reduction 15 recover the low dimensional structure. Although numerous DDR algorithms have been proposed, all assume that data lay on a linear space.

4. The performance with the change of rate for must-link set (d: reduced dimensionality) performance when all constraints are must-link ones. The most probable reason would be that the transformation from must-link constraints into cannot-link constraints can not be performed when the necessary cannot-link constraints lack. This behavior is consistent with the conclusion demonstrated in [9] that cannot-link constraints are more important than must-link constraints in guiding the dimension reduction.

The salient characteristic of this step is that by using as landmarks the local points of a peer we manage to kill two birds with one stone. On one hand we embed the local dataset in Rn while simultaneously each peer derives an approximation of the global dataset. Consequently, each node is able to access global knowledge locally. e. for LMDS a < n). In such case, a network wide landmark selection process can be applied. The simplest way, is to assign a peer n with the role of aggregator and then all peers transmit at least M local points.

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


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