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 info for Advances in Knowledge Discovery and Data Mining, Part II: 14th Pacific-Asia Conference, PAKDD 2010, Hyderabad, India, June 21-24, 2010, Proceedings
Bin Tong is sponsored by the China Scholarship Council (CSC). Subclass-Oriented Dimension Reduction 13 References 1. : Subspace Clustering for High Dimensional Data: A Review. In: SIGKDD Explorations, pp. 90–105 (2004) 2. : Introduction to Statistical Pattern Recognition. Academic Press, San Diego (1990) 3. : Principal Component Analysis. Springer, New York (1986) 4. : Semi-supervised Learning Literature Survey. Technical Report Computer Sciences 1530, University of Wisconsin-Madison (2007) 5. : Semi-supervised Dimensionality Reduction.
Afterwards, retrieve the actual points, calculate their distances from p and retain the kNNs. In order to reduce the required messages we halt the procedure as soon as ck points have been retrieved (in our experiments we set c = 5). Additionally, for each point, we deﬁne a range boundp that enables a queried peer to return only a subset of the points that indexes using Theorem 1. We use as bound the mean distance that a point exhibits from the points of its local dataset. Theorem 1. f , the diﬀerence δ of the l1 norms of the T projections xf ,y f of two points x, y ∈ Rn is upper bounded by where x − y is the points’ euclidean distace.
On the other hand, if CCMapper reads the input data from a sequence, CCMapper outputs
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