By Bin Tong, Einoshin Suzuki (auth.), Mohammed J. Zaki, Jeffrey Xu Yu, B. Ravindran, Vikram Pudi (eds.)
This ebook constitutes the lawsuits of the 14th Pacific-Asia convention, PAKDD 2010, held in Hyderabad, India, in June 2010.
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This booklet constitutes the complaints of the 14th Pacific-Asia convention, PAKDD 2010, held in Hyderabad, India, in June 2010.
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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
Table 1(a) provides the clustering results obtained using k = 8 for the deﬁnition of the NNs for D-Isomap, Isomap and L-Isomap. The results highlight the applicability of D-Isomap in DDM problems as well as the non linear nature of text corpuses. Both ﬂavours of our algorithm produce results marginally equal and sometimes superior to central LSI. The low performance of Isomap and Distributed Knowledge Discovery with Non Linear Dimensionality Reduction 25 L-Isomap should be attributed to the deﬁnition of non-connected NN graphs.
The performance with different numbers of constraints (d: reduced dimensionality) The reason is probably that the feature of discovering the local structure of data points could not help CLPP to outperform PCA. However, our SODRPaC, which also utilizes the manifold regularization due to its property of discovering the local structure, obtains the best performance. We can judge that the new discriminant criterion boosts the performance. It is also presented in Fig. 3d that the performance of SSDR decreases to some extent with the increase of the number of constraints.
Morgan Kaufmann, San Francisco (2002) 5. : Local relevance weighted maximum margin criterion for text classiﬁcation. In: SIAM SDM, pp. 1135–1146 (2009) 6. : Distributed similarity search in high dimensions using locality sensitive hashing. In: ACM EDBT, pp. 744–755 (2009) 7. : Parallelizing the qr algorithm for the unsymmetric algebraic eigenvalue problem. In: SIAM JSC, pp. 870–883 (1994) 8. : What is the nearest neighbor in high dimensional spaces? In: VLDB, pp. 506–515 (2000) 9. : Collective pca from distributed heterogeneous data.