By Wei-Ying Ma (auth.), Mohammed J. Zaki, Jeffrey Xu Yu, B. Ravindran, Vikram Pudi (eds.)
This ebook constitutes the court cases of the 14th Pacific-Asia convention, PAKDD 2010, held in Hyderabad, India, in June 2010.
Read or Download Advances in Knowledge Discovery and Data Mining: 14th Pacific-Asia Conference, PAKDD 2010, Hyderabad, India, June 21-24, 2010. Proceedings. Part I PDF
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Extra info for Advances in Knowledge Discovery and Data Mining: 14th Pacific-Asia Conference, PAKDD 2010, Hyderabad, India, June 21-24, 2010. Proceedings. Part I
See Figures 4(b) and 5(a) for examples. Diﬀerent viewers may deduce diﬀerent numbers of clusters from such poor-quality images, or worse, not be able to estimate c at all. This raises the question of whether we can transform D into a new form D so that the VAT image of D is clearer and more informative about the cluster structure. iVAT and aVAT: Enhanced Visual Analysis 19 In , SpecVAT combines VAT with graph embedding [19,20] to solve this problem. SpecVAT ﬁrst embeds the data into a k-dimensional subspace spanned by the eigenvectors of the normalized Laplacian matrix and then re-computes a new pairwise dissimilarity matrix in the embedding subspace as the input of the VAT algorithm.
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. Recalculating R for the tentative reassignment of item v from cluster Ai to cluster Aj requires time proportional to |Ai | + |Aj | + 2|Iv | when using the inverted neighbor lists.
In: Proc. 3rd SIAM Intern. Conf. on Data Mining, SDM (2003) A Set Correlation Model for Partitional Clustering 15 4. : A density-based algorithm for discovering clusters in large spatial databases with noise. In: Proc. 2nd Int. Conf. on Knowl. Discovery and Data Mining (KDD), pp. 226–231. AAAI Press, Menlo Park (1996) 5. : The relevant-set correlation model for data clustering. Stat. Anal. Data Min. 1(3), 157–176 (2008) 6. : The hardness of k-means clustering. Technical Report CS2007-0890, University of California, San Diego (2008) 7.