New PDF release: 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 e-book constitutes the complaints of the 14th Pacific-Asia convention, PAKDD 2010, held in Hyderabad, India, in June 2010.

Show description

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

Similar data mining books

Download e-book for kindle: Intelligent Soft Computation and Evolving Data Mining: by Leon Shyue-Liang Wang, Tzung-Pei Hong

Because the purposes of knowledge mining, the non-trivial extraction of implicit details in an information set, have improved in recent times, so has the necessity for options which are tolerable to imprecision, uncertainty, and approximation. clever smooth Computation and Evolving info Mining: Integrating complex applied sciences is a compendium that addresses this desire.

New PDF release: Applied Soft Computing Technologies: The Challenge of

This quantity provides the lawsuits of the ninth on-line international convention on gentle Computing in business purposes, hung on the area huge net in 2004. It contains lectures, unique papers and tutorials provided in the course of the convention. The booklet brings jointly awesome study and advancements in smooth computing, together with evolutionary computation, fuzzy common sense, neural networks, and their fusion, and its functions in technological know-how and expertise.

Data mining: know it all - download pdf or read online

This booklet is ready information acquisition and integration, info preprocessing, actual layout for selection help, warehousing, and OLAP, Algorithms: the elemental tools, extra recommendations in choice research, primary thoughts of genetic algorithms, information constructions and algorithms for relocating gadgets varieties.

Download e-book for kindle: Kernel-based Data Fusion for Machine Learning: Methods and by Shi Yu, Léon-Charles Tranchevent, Bart Moor, Yves Moreau

Information fusion difficulties come up usually in lots of various fields. This ebook presents a particular advent to information fusion difficulties utilizing help vector machines. within the first half, this ebook starts with a short survey of additive types and Rayleigh quotient pursuits in computer studying, after which introduces kernel fusion because the additive enlargement of aid vector machines within the twin challenge.

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

Sample text

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 [6], which implements Google’s Map/Reduce paradigm [5].

Download PDF sample

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


by Paul
4.5

Rated 5.00 of 5 – based on 3 votes

Related posts