By Mohammed J. Zaki, Jeffrey Xu Yu, B. Ravindran, Vikram Pudi
This booklet constitutes the lawsuits of the 14th Pacific-Asia convention, PAKDD 2010, held in Hyderabad, India, in June 2010.
Read Online 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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Extra info 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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Fedra: A fast and efficient dimensionality reduction algorithm. In: SIAM SDM, pp. 509–520 (2009) 26 P. Magdalinos, M. Vazirgiannis, and D. Valsamou 13. : Global pca for dimensionality reduction in distributed data mining. In: SDMKD, ch. 19, pp. 327–342. CRC, Boca Raton (2004) 14. : Pca for dimensionality reduction in massive distributed data sets. In: 5th International Workshop on High Performance Data Mining (2002) 15. : A scalable contentaddressable network. In: ACM SIGCOMM, pp. 161–172 (2001) 16.
The mapping of hash identifiers to peer identifiers is accomplished by employing a similarity preserving transformation that depicts a vector from Rf in R1 [6]. For a given vector x, LSH produces an f -dimensional vector; the l1 norm of this vector defines a similarity preserving mapping to R1 . Additionally, it can be proved that the obtained l1 values are generated from the normal distribution f N ( f2 , w μl(xi ) ), where μl(xi ) is the mean value of all points’ Euclidean norm. Conl (v)−μ +2σ l1 sequently, each hash value v is indexed by peer pi = ( 1 4∗σl1l ∗ M )modM .
Candidate set summaries at the current timestamp are output for the computation at the next timestamp as well. Next, each SAMapper in the support assembling job reads input data and accumulates local occurrence frequencies for each candidate sequential patterns. SAMapper generates pairs of