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

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

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 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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Commun. ACM 51(1) (2008) 3. : Document clustering using locality preserving indexing. IEEE TKDE 17(12), 1624–1637 (2005) 4. : Mining the Web: Discovering Knowledge from Hypertext Data. 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.

Pattern Analysis and Machine Intelligence 29(4), 650–664 (2007) 15. html 16. : Multiclass Cancer Diagnosis Using Tumor Gene Expression Signatures. Proceedings of the National Academy of Sciences, 15149–15154 (1998) 17. : Face Recognition Using Laplacianfaces. com Abstract. Data mining tasks results are usually improved by reducing the dimensionality of data. This improvement however is achieved harder in the case that data lay on a non linear manifold and are distributed across network nodes. Although numerous algorithms for distributed dimensionality reduction have been proposed, all assume that data reside in a linear space.

Although this may not be optimal, it does not affect our results, since we report the relative performance of the classifier. 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.

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