Advances in Knowledge Discovery and Data Mining: 14th by Wei-Ying Ma (auth.), Mohammed J. Zaki, Jeffrey Xu Yu, B. PDF

By Wei-Ying Ma (auth.), Mohammed J. Zaki, Jeffrey Xu Yu, B. Ravindran, Vikram Pudi (eds.)

ISBN-10: 3642136567

ISBN-13: 9783642136566

This booklet constitutes the court cases of the 14th Pacific-Asia convention, PAKDD 2010, held in Hyderabad, India, in June 2010.

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Extra resources for Advances in Knowledge Discovery and Data Mining: 14th Pacific-Asia Conference, PAKDD 2010, Hyderabad, India, June 21-24, 2010. Proceedings. Part I

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Fast K -means was excluded in this experiment due to its lack of support for sparse numerical data. Since the number of external classes of these data sets was not as reliable as of the ALOI image data set, we first ran GreedyRSC to estimate the number of natural clusters K. The clustering result of GreedyRSC was used for the initialization of GlobalRSC, while CLUTO was run with the desired number of clusters also set to K. The cosine similarity measure was used for all runs. The clustering scores, averaged over 20 runs, are reported in Table 1.

R(AK ) and set the termination flag halt ← FALSE. (b) Repeat until halt = TRUE: i. Set halt ← TRUE. ii. For each v ∈ S, build the inverted neighborhood I v , where r ∈ I v if and only if v ∈ Qr . iii. For each data item v currently in cluster Ai : A. Build the list C of clusters (other than Ai ) containing at least one item of Qv . B. Tentatively reassign v to each of the Aj in C. C. Using the inverted neighborhood sets, calculate the index j for which the improvement value R(Aj ∪ {v}) + R(Ai \{v}) − R(Aj ) − R(Ai ) is maximized.

B) Repeat until halt = TRUE: i. Set halt ← TRUE. ii. For each data item v currently in cluster Ai : A. Build the list C of clusters (other than Ai ) that contribute items to Qv . B. Tentatively reassign v to each of the Aj in C, and calculate the index j for which the improvement value R(Aj ∪ {v}) + R(Ai \{v})− R(Aj )− R(Ai ) is maximized. C. If the improvement value is positive, reassign v to Aj immediately, adjust the values of R(Aj ) and R(Ai ), and set halt ← FALSE. Fig. 1. A pseudocode description of the basic GlobalRSC variant During each round of the batch phase, building the inverted neighbor sets requires that the values of K(σ 2 + μ2 ) integer variables be copied.

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Advances in Knowledge Discovery and Data Mining: 14th Pacific-Asia Conference, PAKDD 2010, Hyderabad, India, June 21-24, 2010. Proceedings. Part I by Wei-Ying Ma (auth.), Mohammed J. Zaki, Jeffrey Xu Yu, B. Ravindran, Vikram Pudi (eds.)


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