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

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

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

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Additional 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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Increasing contrast) to obtain a more reliable threshold, we transform the image intensities using a “monotonic” function f (txy ) = 1 − exp(−t2xy /σ2 ) where txy denotes the intensity value of the image pixel on the location (x, y), and σ is empirically set as the mean value of all pixel intensities. iVAT and aVAT: Enhanced Visual Analysis 21 Chamfer matching was first proposed by Barrow et al. [25]. Assume that two M point sets are U = {ui }N i=1 and V = {vi }i=1 , the chamfer distance is defined as dcham (U, V) = 1 N min ui − vj .

1. The heuristic can easily be shown to converge within a finite number of steps. 3 Complexity Analysis The algorithm requires storage for the neighbor lists of all n data items, each of which has size proportional to that of the cluster to which it has been assigned. 2 The total space required is of order K i=1 |Ai | . Let μ and σ be respectively the mean and standard deviation of the cluster sizes; in terms of μ and σ, the space required is proportional to K(σ 2 + μ2 ). At the initialization step, the neighborhood list for each data item must be calculated.

Since the neighborhoods must be constructed in sorted order, the total time required for preparing neighborhoods is K i=1 |Ai |(|Ai | + |Ai | log |Ai |) = O((σ 2 + μ2 )K log n) using linear-time methods for determining order statistics [7]. The total time required for initialization is thus O(dn2 +(σ 2 +μ2 )K log n), where d is the cost of computing a single distance. A Set Correlation Model for Partitional Clustering 9 Input: The data set S; the number of desired clusters K; (optionally) a hard initial clustering A = {A1 , A2 , .

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