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 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

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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 pairs as shown in lines 4 to 5. These output pairs are distributed to CCReducers as their inputs. Pairs with the same key are sent to the same CCReducer. By means of the summary at the previous timestamp and the arriving itemset at the current timestamp, each CCReducer is able to generate candidate sequential patterns of each sequence in the current POI. In line 2 of CCReducer, the multiple output variable is used to output candidate set summary at the current timestamp for the future computation.

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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


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