By Mohammed J. Zaki, Jeffrey Xu Yu, B. Ravindran, Vikram Pudi
This e-book constitutes the complaints of the 14th Pacific-Asia convention, PAKDD 2010, held in Hyderabad, India, in June 2010.
Read or Download Advances in Knowledge Discovery and Data Mining, Part II: 14th Pacific-Asia Conference, PAKDD 2010, Hyderabad, India, June 21-24, 2010, Proceedings PDF
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Additional resources for Advances in Knowledge Discovery and Data Mining, Part II: 14th Pacific-Asia Conference, PAKDD 2010, Hyderabad, India, June 21-24, 2010, Proceedings
In: Proc. of the Workshop on Clustering High Dimensional Data and its Applications, Second SIAM International Conference on Data Mining (2002) 14. : Globally Maximizing, Locally Minimizing: Unsupervised Discriminant Projection with Applications to Face and Palm Biometrics. IEEE Trans. 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.
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.
The static and the incremental sequential pattern mining can be viewed as special cases of the progressive sequential pattern mining. ” In fact, mining progressive sequential patterns intrinsically suﬀers from the scalability problem. In this work, we propose a distributed data mining algorithm to address the scalability problem of the progressive sequential pattern mining. The proposed algorithm DPSP, which stands for Distributed Progressive Sequential Pattern mining algorithm, is designed on top of Hadoop platform , which implements Google’s Map/Reduce paradigm .
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